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Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis

Mental health problems are critical and common in medical staff working in Intensive Care Units (ICU) even at the late stage of COVID-19, particularly for nurses. There is little research to explore the inner ...

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Exploring the psychological experience of novice nurses in stomatological hospitals in China: a phenomenological study

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How does strength use relate to burnout among Chinese healthcare professionals? Exploring the mediating roles of beliefs about stress and basic psychological needs satisfaction

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Cognitive flexibility's role in shaping self-perception of aging, body appreciation, and self-efficacy among community-dwelling older women

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Competency scale of quality and safety for greenhand nurses: instrument development and psychometric test

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Status and content of outpatient preoperative education for rectal cancer patients undergoing stoma surgery provided by Japanese wound, ostomy, and continence nurses: a cross-sectional study

Preoperative education can improve postoperative quality of life in patients undergoing stoma surgery. However, the prevalence and when, where, and how preoperative education is implemented are unclear. Theref...

Effects of nurses-led multidisciplinary-based psychological management in spinal surgery: a retrospective, propensity-score-matching comparative study

Patients in spine surgery often have emotional disorders which is caused by multi-factors. Therefore, a multidisciplinary and multimodal intervention program is required to improve emotional disorders during t...

Nurses’ perceptions of how their professional autonomy influences the moral dimension of end-of-life care to nursing home residents– a qualitative study

Over the years, caring has been explained in various ways, thus presenting various meanings to different people. Caring is central to nursing discipline and care ethics have always had an important place in nu...

Challenges of home care: a qualitative study

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A closer look: obsessive-compulsive symptoms among intern nurses amidst COVID-19 pandemic

The distinctive circumstances and socio-cultural context in Egypt make it crucial to explore the psychological well-being of intern nurses amid the COVID-19 pandemic, with a specific focus on obsessive-compuls...

Digital stress perception among German hospital nurses and associations with health-oriented leadership, emotional exhaustion and work-privacy conflict: a cross-sectional study

The use of digital information and communication technologies (ICT) can be accompanied by increased technostress for nursing staff, which in turn can be associated with health consequences. In addition, the us...

Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students’ perception of and intention to use them: a descriptive study

Artificial intelligence (AI)-based healthcare technologies are changing nurses’ roles and enhancing patient care. However, nursing students may not be aware of the benefits, may not be trained to use AI-based ...

Health characteristics and factors associated with transition shock in newly graduated registered nurses: a latent class analysis

Transition shock occurs at a vulnerable time in newly graduated registered nurses’ careers and has a clear impact on both newly graduated registered nurses’ productivity and patient recovery outcomes. Identify...

Nursing students’ attitudes toward intimate partner violence and its relationship with self-esteem and self-efficacy

Understanding nursing students’ attitudes toward Intimate Partner Violence (IPV) is pivotal because it may impact the care and support, they provide victims. This study aimed to explore nursing students’ attit...

A multi-country mixed-method study identifying the association between perceived ethical work climate and problems among critical care nurses

Given the grave ethical tension and dilemmas posed continuously which are aggravated in the intensive care unit context and its related caregiving provision, combined with their impact on critical care nurses’...

Radiation safety compliance awareness among healthcare workers exposed to ionizing radiation

In recent years, there has been a marked growth in the use of ionizing radiation in medical imaging for both diagnosis and therapy, which in turn has led to increased radiation exposure among healthcare workers.

Exploring nurses’ experiences of providing spiritual care to cancer patients: a qualitative study

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Development and effects of advanced cardiac resuscitation nursing education program using web-based serious game: application of the IPO model

The significant rise in cardiac arrest cases within hospitals, coupled with a low survival rate, poses a critical health issue. And in most situations, nurses are the first responders. To develop nursing stude...

Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany

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Determination of nurses’ happiness, hope, future expectations, and the factors influencing them: a descriptive study that can guide policy development to prevent nurse migration*

The happiness and hopefulness of nurses are not only known that contribute to their emotional well-being but also professional creativity, improve the quality of nursing services and organizational performance...

Evaluation of a new concept to improve and organize clinical practice in nursing education: a pilot-study

Nursing students may experience clinical practice as unsafe due to the interactions with patients, fear of making mistakes, lack of clinical experience and supervision, which results in anxiety and stress. Thu...

Variability of clinical practice in the care of the second stage of labor among midwives in Spain

There are recommendations based on scientific evidence on care in the second stage of labor, but it is not known to what degree the professionals comply with these recommendations.

Study protocol for the development, trial, and evaluation of a strategy for the implementation of qualification-oriented work organization in nursing homes

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Outcomes of professional misconduct by nurses: a qualitative study

Professional misconduct by nurses is a critical challenge in providing safe quality care, which can lead to devastating and extensive outcomes. Explaining the experiences of clinical nurses and nursing manager...

Face-to-face versus 360° VR video: a comparative study of two teaching methods in nursing education

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Evaluating person-centered care in neurological outpatient care: a mixed-methods content validity study

Person-centered care (PCC) is gaining increased attention. PCC concerns the whole person behind the disease and can improve care for people with long-term conditions such as multiple sclerosis (MS) and Parkins...

Relationship between medication burden and medication experience in stable patients with schizophrenia: the mediating effect of medication belief

Individuals with schizophrenia require prolonged antipsychotic medication treatment. But more than 50% of individuals with schizophrenia experience adverse medication experiences during their antipsychotic tre...

Enhancing feedback by health coaching: the effectiveness of mixed methods approach to long-term physical activity changes in nurses. An intervention study

Although knowledge of the barriers and motivators to physical activity participation among nurses is increasing, the factors influencing motivation methods’ effectiveness are not completely defined. This study...

Individual and organizational interventions to promote staff health and well-being in residential long-term care: a systematic review of randomized controlled trials over the past 20 years

Staff in residential long-term care (RLTC) experience significant physical and mental work demands. However, research on specific interventions to promote staff health and well-being in RLTC facilities is limi...

Thriving at work as a mediator of the relationship between psychological resilience and the work performance of clinical nurses

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The relationship between organizational dehumanization and work engagement: the mediating effect of nurses’ work stress

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Job crafting, positive psychological capital, and social support as predictors of job embeddedness on among clinical nurses- a structural model design

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Inductive process of moral distress development in viewpoints from surgical nurses: a mixed-method study

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Assessment of preventive practices towards hepatitis B infection among nursing students in Bangladesh: role of knowledge, attitudes and sociodemographic factors

Globally, hepatitis B infection (HBI) poses a substantial public health concern and healthcare workers, including nursing students, are at a higher risk of contracting this disease. Thus, the study aimed to as...

Assessment of patient safety culture in Moroccan primary health care: a multicentric study

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Stressors, emotions, and social support systems among respiratory nurses during the Omicron outbreak in China: a qualitative study

Respiratory nurses faced tremendous challenges when the Omicron variant spread rapidly in China from late 2022 to early 2023. An in-depth understanding of respiratory nurses’ experiences during challenging tim...

Analysis of cybersickness in virtual nursing simulation: a German longitudinal study

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Prevalence and characters of post-acute COVID-19 syndrome in healthcare workers in Kashan/Iran 2023: a cross-sectional study

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Exploring advanced clinical practitioner perspectives on training, role identity and competence: a qualitative study

Advanced Clinical Practitioners (ACPs) are a new role that have been established to address gaps and support the existing medical workforce in an effort to help reduce increasing pressures on NHS services. ACP...

Improvement and implementation of central sterile supply department training program based on action research

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Translation and psychometric evaluation of the Persian version of the nurses’ quality of life scale: a validation study in Iran

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Cultural sensitivity and associated factors among nurses in southwest Ethiopia: a cross-sectional study

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Nurses’ motivation for performing cardiopulmonary resuscitation: a cross-sectional study

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The biosafety incident response competence scale for clinical nursing staff: a development and validation study

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Prevalence of medication errors and its related factors in Iranian nurses: an updated systematic review and meta-analysis

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The impact of mentorship program on the level of anxiety and pre-internship exam scores among Iranian senior nursing students

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Compassionate care during the COVID-19 pandemic

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The quality of transitional care is closely related to the health outcomes of patients, and understanding the status of transitional care for patients is crucial to improving the health outcomes of patients. T...

Knowledge, attitudes, and practices toward Patient Safety among nurses in health centers

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BMC Nursing

ISSN: 1472-6955

The essentials of nursing leadership: A systematic review of factors and educational interventions influencing nursing leadership


  • 1 Faculty of Nursing, Edmonton Clinic Health Academy, University of Alberta, 11405 87 Ave NW, Edmonton, AB T6G 1C9, Canada. Electronic address: [email protected].
  • 2 Department of Nutrition, Dietetics and Food, School of Clinical Sciences at Monash Health, Monash University, Level 1, 264 Ferntree Gully Rd, Notting Hill, VIC 3168, Australia.
  • 3 Faculty of Nursing, Edmonton Clinic Health Academy, University of Alberta, 11405 87 Ave NW, Edmonton, AB T6G 1C9, Canada.
  • 4 Faculty of Nursing, Edmonton Clinic Health Academy, University of Alberta, 11405 87 Ave NW, Edmonton, AB T6G 1C9, Canada; Technical High School of Campinas, State University of Campinas (UNICAMP), Barão Geraldo, Campinas - São Paulo 13083-970, Brazil.
  • PMID: 33383271
  • DOI: 10.1016/j.ijnurstu.2020.103842

Background: Nursing leadership plays a vital role in shaping outcomes for healthcare organizations, personnel and patients. With much of the leadership workforce set to retire in the near future, identifying factors that positively contribute to the development of leadership in nurses is of utmost importance.

Objectives: To identify determining factors of nursing leadership, and the effectiveness of interventions to enhance leadership in nurses.

Design: We conducted a systematic review, including a total of nine electronic databases.

Data sources: Databases included: Medline, Academic Search Premier, Embase, PsychInfo, Sociological Abstracts, ABI, CINAHL, ERIC, and Cochrane.

Review methods: Studies were included if they quantitatively examined factors contributing to nursing leadership or educational interventions implemented with the intention of developing leadership practices in nurses. Two research team members independently reviewed each article to determine inclusion. All included studies underwent quality assessment, data extraction and content analysis.

Results: 49,502 titles/abstracts were screened resulting in 100 included manuscripts reporting on 93 studies (n=44 correlational studies and n=49 intervention studies). One hundred and five factors examined in correlational studies were categorized into 5 groups experience and education, individuals' traits and characteristics, relationship with work, role in the practice setting, and organizational context. Correlational studies revealed mixed results with some studies finding positive correlations and other non-significant relationships with leadership. Participation in leadership interventions had a positive impact on the development of a variety of leadership styles in 44 of 49 intervention studies, with relational leadership styles being the most common target of interventions.

Conclusions: The findings of this review make it clear that targeted educational interventions are an effective method of leadership development in nurses. However, due to equivocal results reported in many included studies and heterogeneity of leadership measurement tools, few conclusions can be drawn regarding which specific nurse characteristics and organizational factors most effectively contribute to the development of nursing leadership. Contextual and confounding factors that may mediate the relationships between nursing characteristics, development of leadership and enhancement of leadership development programs also require further examination. Targeted development of nursing leadership will help ensure that nurses of the future are well equipped to tackle the challenges of a burdened health-care system.

Keywords: Interventions; Leadership; Nursing workforce; Systematic Review.

Copyright © 2020. Published by Elsevier Ltd.

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  • Published: 27 March 2024

Evaluating methods for risk prediction of Covid-19 mortality in nursing home residents before and after vaccine availability: a retrospective cohort study

  • Komal Aryal 1 , 2 ,
  • Fabrice I. Mowbray 3 ,
  • Anna Miroshnychenko 1 ,
  • Ryan P. Strum 1 ,
  • Darly Dash 1 ,
  • Michael P. Hillmer 4 , 5 ,
  • Kamil Malikov 5 ,
  • Andrew P. Costa 1 , 2 &
  • Aaron Jones 1 , 2  

BMC Medical Research Methodology volume  24 , Article number:  77 ( 2024 ) Cite this article

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SARS-CoV-2 vaccines are effective in reducing hospitalization, COVID-19 symptoms, and COVID-19 mortality for nursing home (NH) residents. We sought to compare the accuracy of various machine learning models, examine changes to model performance, and identify resident characteristics that have the strongest associations with 30-day COVID-19 mortality, before and after vaccine availability.

We conducted a population-based retrospective cohort study analyzing data from all NH facilities across Ontario, Canada. We included all residents diagnosed with SARS-CoV-2 and living in NHs between March 2020 and July 2021. We employed five machine learning algorithms to predict COVID-19 mortality, including logistic regression, LASSO regression, classification and regression trees (CART), random forests, and gradient boosted trees. The discriminative performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) for each model using 10-fold cross-validation. Model calibration was determined through evaluation of calibration slopes. Variable importance was calculated by repeatedly and randomly permutating the values of each predictor in the dataset and re-evaluating the model’s performance.

A total of 14,977 NH residents and 20 resident characteristics were included in the model. The cross-validated AUCs were similar across algorithms and ranged from 0.64 to 0.67. Gradient boosted trees and logistic regression had an AUC of 0.67 pre- and post-vaccine availability. CART had the lowest discrimination ability with an AUC of 0.64 pre-vaccine availability, and 0.65 post-vaccine availability. The most influential resident characteristics, irrespective of vaccine availability, included advanced age (≥ 75 years), health instability, functional and cognitive status, sex (male), and polypharmacy.


The predictive accuracy and discrimination exhibited by all five examined machine learning algorithms were similar. Both logistic regression and gradient boosted trees exhibit comparable performance and display slight superiority over other machine learning algorithms. We observed consistent model performance both before and after vaccine availability. The influence of resident characteristics on COVID-19 mortality remained consistent across time periods, suggesting that changes to pre-vaccination screening practices for high-risk individuals are effective in the post-vaccination era.

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The COVID-19 pandemic led to an exponential surge in the number of deaths within nursing homes (NH) [ 1 , 2 , 3 , 4 ]. In 2020, 63% of all deaths in the NH were attributed to COVID-19 among NH resident deaths in Canada [ 5 ]. NH residents have been disproportionately affected by COVID-19 illness due to their complex health and physical care needs, coupled with increasing fraility [ 6 ]. SARS-CoV-2 vaccines have been effective in reducing hospitalization, COVID-19 symptoms, and mortality for NH residents, and NH residents were prioritized during vaccine rollout [ 7 , 8 , 9 , 10 , 11 ]. Nearly 80% of NH residents had received one dose of a SARS-CoV-2 vaccine in Canada by the end of January 2021 [ 12 ]. Even after vaccination however, residents with high-risk profiles can still experience poor outcomes from COVID-19, including death [ 7 ]. Numerous resident characteristics (e.g., age, gender, cognitive status, and physical functioning) have been examined as prognostic factors for COVID-19 mortality [ 13 , 14 ]. However, little is known about how the predictability of COVID-19 mortality changed due to the vaccine rollout.

Regression-based and tree-based machine learning models have been widely used in health and health services research. Advanced machine learning algorithms demonstrate remarkable capability in identifying high-risk subpopulations, particularly when predictors exhibit intricate interaction effects [ 15 ]. As a result, these methods have gained considerable popularity in research involving complex and vulnerable populations, such as NH residents. However, a unanimous consensus on the most suitable method to discriminate outcomes remains elusive, primarily due to the susceptibility of tree-based models to overfitting, which compromises the model’s generalizability [ 16 , 17 , 18 , 19 ]. Accurate mortality prediction at the individual NH resident level could greatly benefit healthcare professionals in prioritizing medical care and enabling efficient resource planning.

Our study aimed to utilize different machine learning methods to compare COVID-19 mortality prognostication in NH residents before and after vaccine availability. Our objectives were to establish the accuracy of various machine learning models, examine changes to model performance, and identify resident characteristics that have the strongest associations with 30-day COVID-19 mortality, before and after availability.

Study design

We conducted a population-based retrospective cohort study analyzing data from all NH facilities across Ontario, Canada. This study was reviewed and approved by the Hamilton Integrated Research Ethics Board (HiREB # 10,959-C). To ensure accurate reporting, we followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement guidelines for this cohort study.

Data sources

Four population-level health administrative databases were examined. The Continuing Care Reporting System (CCRS) is a data repository of clinical assessments that are completed for each NH resident using the Resident Assessment Instrument - Minimum Data Set (RAI-MDS) 2.0 [ 20 ]. Residents receive a RAI-MDS assessment upon admission into the facility and then every three months thereafter. The CCRS also records changes to resident status such as discharges from NH, death died in the facility, or during a hold-bed period when a resident is temporarily elsewhere such as the hospital. The Ontario Laboratories Information System (OLIS) records lab test orders and results from hospitals, community labs and public health labs, including those for COVID-19, for which we used the specimen collection date. The Ontario Integrated Public Health Information System (IPHIS) documents public health cases, including COVID-19, for which we used the episode start date, and records dates of death if the resident died during the COVID-19 episode. We used the Discharge Abstract Database (DAD) to identify all those who were admitted to the hospital and recorded as deceased during their stay. Datasets used were linked and deidentified by the Ontario Ministry of Health (MOH).

Study participants

We included all residents in our cohort who were admitted into the NH and stayed at an NH for one or more days between March 7, 2020, and July 31, 2021. We excluded residents if they had no complete RAI-MDS 2.0 assessment or resided outside of Ontario. For residents who were admitted to an NH more than once, we utilized the first assessment per time period to avoid correlated data among residents. Our study only utilized assessments completed on or before the positive SARS-CoV-2 infection date [ 21 ].

Outcome measure & exposure periods

Our primary outcome was 30-day mortality following a laboratory-verified positive SARS-CoV-2 test. We examined positive cases during two time periods. The first time period lasted from the start of the pandemic to the beginning of vaccine availability (March 7, 2020, to December 31, 2020) which was selected based on the closest date to the first positive SARS-CoV-2 infection in NH and the first reported outbreak [ 22 ]. The second time period lasted from vaccine availability until the end of the study period (January 1, 2021, to July 31, 2021). We selected January 1, 2021 as our second time period because it approximates the start of vaccine rollout in the NH [ 23 ].

Residents in both time periods were followed for 30 days to measure the primary outcome. If residents tested positive for SARS-CoV-2 more than once in each study period, we included only the first instance to avoid correlated data. Patients who tested positive during the first 30 days of the second time period were only included if there was no documentation of a positive COVID-19 test in the previous 30 days.

Resident characteristics

Resident characteristics for inclusion in the machine learning models were selected based on their availability in our data sources, clinical expertise and prior literature [ 3 , 24 , 25 , 26 , 27 , 28 ]. Resident characteristics came from the completed MDS 2.0 assessments. These characteristics include demographic, clinical, and social characteristics reported at the NH facility that are expected to be associated with COVID-19 mortality. The methods employed for resident character selection in the study were not statistically driven and are described in detail in our previous work [ 29 ].

Statistical analysis

Descriptive statistics were reported using measures of frequency and central tendency to compare residents who died due to COVID-19 both before and after vaccine availability. Variable selection was performed a priori using both theoretical and clinical methods based on existing literature [ 29 ]. We used five machine learning algorithms to predict COVID-19 death. These included logistic regression, LASSO logistic regression, classification and regression tree (CART), random forests, and gradient boosted trees (GBT). Data were screened for the presence and pattern of missingness. Only five (0.03%) cases of missing data were present for one predictor variable and these cases were deleted within each analysis.

We performed hyperparameter tuning for each non-logistic regression model to determine the optimal set of parameters for each machine learning model. Tuning was performed using 10-fold cross-validation with a 1000-iteration random grid search over the parameter space, selecting the parameters that maximized the area under the receiver operating characteristic curve (AUC). All tuning and testing were performed independently for the two vaccination periods. Final performance was determined by calculating the AUC for each model using an independent and identical 10-fold cross-validation and by computing F1 scores and balanced accuracy. Permutation methods were used to calculate variable importance by repeating ( n  = 50) randomly permutated values of each predictor in the dataset and re-evaluating the model’s performance, which was not nested inside the cross-validation [ 30 ]. The average difference in the AUC was used to measure variable importance, with negative values of greater magnitude indicating more importance. Model calibration was assessed visually using calibration plots. Data were managed and analyzed using R-Studio 4.2.2.

Sensitivity analysis

We conducted a sensitivity analysis by excluding January 2021, during which time the first dose was being rolled-out to NH residents. We recalculated performance statistics for logistic regression and LASSO regression. Since fewer infections occurred between February 2021 to July 2021, complex machine learning models including random forests, CART, and GBT were not conducted in the sensitivity analysis.

There were 14,977 NH residents who tested positive for a COVID-19 infection during the study period. A total of 11,291 NH residents tested positive for COVID-19 before vaccine availability and 3686 NH residents tested positive after vaccine availability. The median time (25th -75th percentiles) from the MDS 2.0 assessment date to the COVID-19 date was 44 days (22–67).

Table  1 displays a comprehensive list of resident characteristics for NH residents who were diagnosed with COVID-19 before and after vaccine availability. Most residents were female (65.8%; 67.6%), 65 + years (92.6%; 93.6%), and were diagnosed with dementia (52.8%; 52.7%) in the before and after vaccine availability groups, respectively. There were 2937 (26.0%) NH residents who died before vaccine availability and 727 (19.7%) NH residents who died after vaccine availability.

Model performance

Table  2 displays the final model performance from the five machine learning models. The cross-validated AUC performance ranged from 0.64 to 0.67, irrespective of vaccination period. Across all models, GBT had the highest discrimination ability with an AUC of 0.67 for both the before and after vaccination periods, although the discriminative accuracy was not significantly different from the other machine learning models, excluding CART ( p  < .05). Our CART model had the lowest discrimination ability both before and after the vaccination periods compared to logistic regression, random forests, and gradient-boosted trees. F1 scores and balanced accuracy are reported in Appendix A and model calibration are presented in Appendix B . The range of model parameters are reported in Appendix C .

Associations with COVID-19 mortality

Approximately half (11/20) of the resident characteristics contributed to the performance of all five models (Appendix D ). On average, the five most influential resident characteristics both before and after vaccine availability were being 85 years older, being aged 75–84, being male, deteriorating ADLs, and having a high score on the CHESS scale (i.e., health instability) (Fig.  1 ). The least influential resident characteristics across all five models both before and after vaccine availability, having a headache, having a fever, experiencing anxiety, having cancer, congestive heart failure, and having respiratory disease. Overall, there was little difference in the variable importance before and after vaccine availability and between the five models.

figure 1

The Average Inverse Variable Importance for all Resident Characteristics Before and After Vaccine Availability. * Respiratory Disease: Chronic Obstructive Pulmonary Disease, Emphysema, Asthma, & Dyspnea ** Nutrition Risk: Decreased appetite, weight loss, & dehydration

There were significantly fewer cases in the February 1, 2021, to July 31, 2021 ( n  = 828) than January 1, 2021 to July 31, 2021 ( n  = 3686). Analysis showed similar but slightly higher AUC values for logistic regression before (0.68) and after (0.69) vaccine availability. Similarly, the AUC values for LASS0 regression before (0.66) and after (0.68) were slightly higher after vaccine availability.

We used one statistical model and four machine learning models to examine 30-day COVID-19 mortality among NH residents before and after vaccine availability in Ontario, Canada. All models exhibited similar predictive accuracy both between models and across the COVID-19 vaccine availability time periods. Approximately half of resident characteristics were informative in identifying residents at high-risk of COVID-19 mortality. These factors remained consistent regardless of vaccine availability in all models.

Our study highlights a ceiling effect on the discriminative ability of machine learning algorithms when using routinely collected administrative data compared to the statistical logistic regression model. While GBT models can accommodate complex patterns within the data, they are computationally complex, and their “black box” nature makes them less appealing to clinical audiences. Prior works have demonstrated similar discriminative accuracies between GBT and logistic regression in complex older adults [ 30 , 31 , 32 ]. Further, our study contributes evidence to prior work demonstrating that the use of complex supervised machine learning algorithms is unlikely to out-perform standard regression models using highly structured data [ 33 ]. We conducted a comparative analysis of the discriminability of machine learning methods before and after COVID-19 vaccine availability. We found no discernible difference in the performance of these models based on vaccine availability.

Previous studies have reported higher AUC values when predicting 30-day COVID-19 mortality [ 34 ]. However, many of these studies focused on 30-day COVID-19 mortality within broad populations, in which age is a highly discriminative predictor of mortality, a consistent result of our study. For example, a study by Hippisley-Cox et al., [ 26 ] focused on a broad population of all adults in England and did not assess the risk of COVID-19 mortality for the NH population. In contrast, our study specifically aims to predict COVID-19 mortality among older, frail nursing home residents. The limited heterogeneity in our samples makes it more challenging to discern individuals who are at a higher risk of death.

We sought to evaluate whether the significance and magnitude of the resident characteristics in these models differed between vaccine availability periods. The important resident characteristics in our model were older age, male sex, and deteriorating ADL status with age being the most influential. However, our results indicate that there is little difference in resident characteristics influencing COVID-19 mortality based on vaccine availability. NH resident characteristics alone were not sufficiently able to determine which residents were at greatest risk of COVID-19 mortality, as evidenced by their relatively weak AUC values, irrespective of time period.

From our research, it is evident that pre-vaccination prognostic scores and models are still informative of post-vaccination scores and models could be effective when employed post vaccination rollout. Existing practices to identify residents at high-risk of COVID-19 death likely do not need to be adjusted. This finding can help determine future care plans for both vaccinated and unvaccinated NH populations. Future studies should determine if COVID-19 risk factors remain stable for older individuals living in congregate care settings such as retirement homes.


We leveraged a population-level database of all NH residents across Ontario and reported on a wide array of resident factors and geriatric syndromes known to be prognostic of mortality post-SARS-CoV-2 infection. However, we were limited to secondary data collected across databases. Undocumented resident characteristics, such as ethnicity or race, may influence mortality but are not recorded in the RAI-MDS 2.0. Our databases did not capture the accurate date or type of vaccine received by NH residents and thus were unable to stratify based on actual vaccination type. These predictors may have been informative in predicting COVID-19 mortality, but we were unable to include them in our model. Our analysis began during the early stages of COVID-19 and some residents may not have had a COVID-19 test before dying results in some COVID-19 deaths may not being captured. The discriminative accuracy of statistical models was fair despite having a panel of prognostic factors known to influence 30-day COVID-19 mortality. However, the use of prognostic models with this level of discriminative ability in population-level research is common, considering the difficulties of predicting within a complex-adaptive system [ 35 , 36 ].

Conclusions and implications

Our study determined that all statistical and machine learning algorithms examined displayed similar predictive accuracy. This suggests that there would be no benefit in choosing a more complex tree-based model over standard regression for these data sources. Overall, the performance of the models did not differ before and after vaccine availability, indicating that vaccine uptake did not change COVID-19 mortality prognostication. Resident characteristics influencing 30-day COVID-19 mortality are similar both before and after vaccine availability. The stability of risk factors and performance between vaccination periods suggests that models generated to predict COVID-19 mortality pre-vaccination are valid for use in the post-vaccination era.

Data availability

Data access is governed separately by the Ontario Personal Health Information Protection Act and held securely at McMaster University. Analytic coding is available upon request from the authors with appropriate approvals to protect the security of source the data architecture.

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Thank you to Dr. Jeff Poss for assisting with data acquisition.

This work was supported in part by grants from the Canadian COVID-19 Immunity Task Force and Public Health Agency of Canada (PHAC) awarded to APC and DMB (2021-HQ-000138). APC is supported by the Schlegel Chair in Clinical Epidemiology and Aging at McMaster University. DD holds a doctoral scholarship through the Canadian Institutes of Health Research (CIHR) (grant #FBD-181577). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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All authors have read and approved the submission of this manuscript. Komal Aryal and Dr. Aaron Jones contributed to the study concept and design. Komal Aryal, Dr. Andrew Costa, Dr. Aaron Jones, Dr. Kamil Malikov, and Dr. Michael P. Hillmer contributed to data acquisition. Dr. Fabrice I. Mowbray and Ryan P. Strum contributed to the clinical conceptualization of the analysis. Komal Aryal, Anna Miroshnychenko and Darly Dash contributed to the descriptive analysis of the manuscript. Komal Aryal contributed to the data analysis and interpretation. Komal Aryal drafted the manuscript. All authors contributed to critical revisions of the manuscript for intellectual content and approved the final version to be published. Drs. Andrew Costa and Aaron Jones provided study supervision.

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Aryal, K., Mowbray, F.I., Miroshnychenko, A. et al. Evaluating methods for risk prediction of Covid-19 mortality in nursing home residents before and after vaccine availability: a retrospective cohort study. BMC Med Res Methodol 24 , 77 (2024). https://doi.org/10.1186/s12874-024-02189-3

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Workforce strategies during the first wave of the COVID-19 pandemic: a retrospective online survey at intensive care units in Germany

  • Lara C. Stroth 1 ,
  • Franziska Jahns 1 ,
  • Berit Bode 1 ,
  • Maike Stender 1 ,
  • Michelle Schmidt 2 , 3 ,
  • Heiko Baschnegger 4 ,
  • Nurith Epstein 5 ,
  • Benedikt Sandmeyer 4 &
  • Carla Nau 1  

BMC Health Services Research volume  24 , Article number:  407 ( 2024 ) Cite this article

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As the COVID-19 pandemic swept across the globe at the beginning of 2020, healthcare systems were forced to rapidly adapt and expand to meet the sudden surge in demand for intensive care services. This study is the first systematic analysis of the strategies employed by German hospitals to recruit personnel and expand bed capacities during the first wave of the pandemic, and to evaluate the effectiveness of those recruitment measures.

152 German hospitals with intensive care capacities were selected and invited to participate in an online-based retrospective survey. Factors like the geographic distribution, individual COVID burden and level of care were considered for inclusion in the sample. The data were analyzed descriptively.

A total of 41 hospitals participated in the survey. The additional demand for intensive care beds was met primarily by activating intensive care beds that were previously considered as non-operational in existing intensive care units (81% of respondents) and by upgrading recovery rooms (73%). The physician staffing requirements were met at approximately 75%, while the nursing staffing requirements were only met by about 45%. Staffing needs were met through reallocations/transfers (85%), staff recruitment from parental leave or retirement (49%), increased hours worked by internal staff (49%), new staff hiring (44%) and increased use of temporary staff (32%). Staff reallocations/transfers to critical care within a hospital were rated as the most effective measure. In this context, specialized personnel mostly from anesthesiology departments were appointed to intensive care medicine.


Despite multiple recruitment efforts, the pandemic has exacerbated the nursing staff shortage. The reallocation of existing staff within hospitals was a key element in covering the staffing needs. However, additional measures and efforts are required in order to ensure that critically ill patients can be cared for without compromise. The results of this study may have important implications for healthcare providers and policymakers, offering an evidence-based foundation for responding to future public health emergencies with agility, efficiency, and success.

Peer Review reports

The COVID-19 pandemic posed substantial challenges to the world and Germany in early 2020. Expanding intensive care capacities and recruiting additional staff were two key issues for hospitals in coping with the crisis.

In Germany, the first case of a SARS-CoV-2 infection was reported at the end of January 2020 [ 1 ]. The first larger outbreaks then occurred as a result of local festivities (e.g., carnival) in mid-February, resulting in increased numbers of cases in individual counties (e.g., Heinsberg) and initiating the onset of the first COVID-19 wave (calendar week 10/2020–20/2020) [ 2 ]. As a result of the infection incidence worldwide, the World Health Organization (WHO) declared the outbreak a pandemic on March 11, 2020.

In view of the expected increase in the number of infections, there was an urgent need to expand technical and personnel intensive care capacities as quickly as possible. The present study seeks to determine the strategies that were used in German hospitals and to rate their effectiveness.

Due to the dynamics of the infectious event and the novel disease that hospitals were confronted with, the first wave is to be considered as particularly critical and required hospitals to quickly and constantly adjust their resources. The pandemic had a severe impact on staff capacities in all German hospitals and necessitated the mobilization of additional medical and nursing staff on a short-term (and temporary) basis to ensure/maintain the care of critically ill patients. This was particularly challenging due to the shortage of nursing staff that existed even before the pandemic. Approximately 28,000 specialists (2019) in geriatric, health and nursing care were lacking [ 3 ]. In intensive care, 53% of hospitals reported to have staffing problems (as of fall 2016), with an average of 4.7 full-time positions not being filled [ 4 ]. A total of 3,150 full-time positions in intensive care remained vacant nationwide with an increasing tendency. Hospitals with staffing problems in the medical service of their intensive care units (ICUs) (29%) had a mean of 1.7 full-time positions vacant. Understaffing of nursing staff in ICUs can have serious consequences for patient care and is associated with an increased risk of mortality, hospital-related infections and pressure wounds [ 5 , 6 ]. Due to lack of child care, staff’s own illness, or quarantine, the pandemic led to additional staff absences. Consequently, alternative concepts for staff recruitment (and training) needed to be developed.

In the context of extending intensive care capacities, the Federal Ministry of Health addressed hospitals in an open letter on March 13, 2020, asking them to recruit additional staff. It also appealed to postpone scheduled surgeries and interventions to build up additional provisional bed and treatment capacities [ 7 ]. At the same time, hospitals were assured of financial compensation for the resulting additional economic burden. In addition, a bonus was introduced for each additional intensive care bed provisionally placed or kept available. The relevant legal framework for this was laid down in the COVID-19 Hospital Relief Act ( COVID-19-Krankenhausentlastungsgesetz ), which came into force on March 27, 2020. Even before the pandemic, Germany had a very high density by international standards, with 33.9 intensive care beds per 100,000 inhabitants [ 8 ]. Capacities were significantly lower, for example, in Spain and Italy, which were severely affected at the beginning of the pandemic, with 9.7 and 8.6 per 100,000 inhabitants, respectively. In addition to measures to cushion the economic consequences, regulations were also enacted that allowed hospitals a greater scope of action in workforce planning, such as the suspension of the Nursing Staff Lower Limit Ordinance ( Pflegepersonaluntergrenzen-Verordnung, PpUGV ) [ 9 ] and the relaxation of the Working Hours Act for certain sectors with the introduction of the COVID-19 Working Hours Ordinance ( COVID-19 Arbeitszeitverordnung, COVID-19-ArbZV ) [ 10 ].

As a result of the acute information demand on the hospitals’ management of COVID-19, the WHO Regional Office for Europe and the German Society of Hospital Disaster Response Planning and Crisis Management ( Deutsche Arbeitsgemeinschaft Krankenhaus Einsatzplanung, DAKEP ), among others, published comprehensive recommendations to support hospitals in the preparation and adjustment of emergency plans [ 11 , 12 ]. Two recent cross-country comparisons of European countries, for example, show that all states used a variety of measures to create sufficient physical infrastructure and to increase workforce surge capacity at the beginning of the pandemic [ 13 , 14 ]. All countries designated COVID-19 units and expanded hospital and surge capacities by setting-up additional acute and ICU beds within existing facilities [ 13 ]. In addition, Germany established a nationwide intensive care bed registry [ 15 ] and carried out inter-hospital transport of COVID-19 patients, including patient uptakes from abroad [ 13 , 16 ]. With respect to the recruitment of additional staff, common strategies of countries were to augment the capacity of available health workers and the recruitment of medical and nursing students [ 14 ]. For Germany, redeployment of personnel who have already retired, initiatives for trained foreign personnel and calls for volunteers were also described. The cross-country comparisons mentioned here report individual measures with reference to country examples but do not provide an overall view of the countries in detail. Site-specific studies for Germany are rare thus far. The only published study in this context by Köppen et al. [ 17 ] analyzed pandemic preparedness planning and action at the federal and state level in Germany and found that measures to expand workforce capacity varied widely among the states. The analyses were based on data from websites of the German Federal and State Ministries for Health and of public health facilities. As with the cross-country comparisons, there is a lack of an overall overview here as well. In addition, we are not aware of any study that provides information on the extent to which measures described were actually applied and how their effectiveness was assessed.

In this study, we therefore systematically evaluated local concepts for expansion for intensive care bed capacity and staff recruitment during the first wave of the COVID-19 pandemic in Germany based on individual first-hand responses from hospitals themselves. The aims of our study were (1) to gain an overview of staffing in intensive care as well as the instruments/measures that were used to meet the increased staffing requirement during the first wave of the pandemic and (2) to assess how effective these strategies were perceived in practice, in order (3) to derive recommendations for future pandemics or crisis.

The survey on recruitment was part of the nationwide survey study “ICU-Response”, which used a cross-sectional design and aimed to systematically assess local approaches to staff recruitment, training and safety management in the context of the first wave of the COVID-19 pandemic. This publication concentrates on the analysis of recruitment strategies; the results on training and safety management are reported elsewhere (e.g., [ 18 ]). ICU-Response was conducted as part of the national collaborative project egePan Unimed “Development, Testing and Implementation of regionally adaptive health care structures and processes for pandemic management guided by evidence and led by university clinics” in the University Medicine Network ( Netzwerk Universitätsmedizin, NUM ).

The study was approved by the Ethics Committee (EK 459/20) of the University Hospital RWTH Aachen.

The national intensive care register of the German Interdisciplinary Association for Intensive Care and Emergency Medicine ( Deutsche Interdisziplinäre Vereinigung für Intensiv- und Notfallmedizin, DIVI ; www.intensivregister.de ) was used to identify all hospitals with intensive care capacities in Germany (> 1,200). Of those, a representative sample of 152 hospitals in total was formed. At first, an equal number of hospitals was selected from three regions (50 North, 48 Central, 54 South), each representing about one third of the nation’s population. While the North included the federal states of Lower Saxony, Schleswig-Holstein, Mecklenburg-Western Pomerania, Berlin, Hamburg, Bremen, Brandenburg, Saxony and Saxony-Anhalt, the central region comprised Thuringia, Hesse and North-Rhine Westphalia. The federal states of Bavaria, Baden-Wuerttemberg, Rhineland-Palatinate and Saarland belonged to the southern region.

Then, the hospitals’ level of care and COVID-19 burden were taken into consideration: Hospitals in Germany are assigned to different levels of care according to their specialization and the services they offer [ 19 ]. Depending on the federal state, three or four levels are distinguished. We attempted to map the proportions of hospital types and included 41 university hospitals or tertiary care hospitals, 40 secondary care hospitals, and 71 primary or basic care hospitals/hospitals with narrow specialization. To identify different levels of COVID-19 patient load, registry data from the Robert Koch Institute (RKI) for the period from January 1 to June 30, 2020, which were made available upon request, were used. Based on these, hospitals were grouped into hospitals with low (0–19), medium (20–59), or high (≥ 60) COVID-19 ICU patient burden. As hospitals with a high volume of COVID-19 patients ( N  = 52) were of particular interest, all of them were included in the survey without exception. The sample was supplemented by 49 hospitals with medium and 51 hospitals with low COVID-19 patient volumes.

Online survey

The hospitals selected were invited to participate in the ICU-Response Survey via email with a personal letter in late March/early April 2021. They were asked to provide the name of a central contact person from their hospital’s critical care department for further correspondence. The central contacts were then contacted by email, provided with the questionnaire on recruitment for preview and asked to complete it online via SoSci Survey [ 20 , 21 ]. Hospitals that had not responded after the initial invitation were reminded by email and/or contacted via telephone. Data collection was conducted between March 24 and June 20, 2021.

Outcome parameters / study variables

The questionnaire was developed purely empirically by the authors based on their COVID-19/clinical experience, their experience in human resource management and in dialogue with further project collaborators. It was provided in German language and comprised a total of 24 questions on the intensive care bed situation (5 items), critical care staffing situation (6 items), recruitment strategies (6 items) and reallocation/shifting of personnel, that were closed-ended or semi-closed. The types of closed-ended questions used included trichotomous, single-choice and multiple-choice questions, as well as a Likert scaled question assessing recruitment methods. The structure of the questionnaire and outcome parameters are presented in Table  1 ; for the complete questionnaire (German original version and English translated version), please see Additional file 1 .

Data analysis

Data analysis was performed using MS Excel 2019; figures were prepared with GraphPad Prism 5 (GraphPad Software, San Diego, CA, USA). Due to the exploratory nature of our study and research questions, we mainly performed descriptive data analysis. For hospitals that had called up the questionnaire several times, only the completed data set was included in the analysis. Data sets that had not been completed by the central contact persons were excluded. Questions that had been answered by less than 50% of participating clinics were also excluded from the analysis.

For reasons of comparison, the ratio of the number of full-time positions and the number of intensive care beds was calculated for each occupational group and hospital. To present the situation regarding personnel before the pandemic, the staffing data were related to the number of ICU beds set up (as of January 1, 2020); for the additional staffing requirements during the pandemic, the total number of currently operable ICU beds (low-care and high-care; as of April 24, 2020) served as reference value. The key data on the number of intensive care beds were derived from the DIVI Intensive Care Bed Registry and were made available by the RKI.

Data on full-time positions, number of employees and duration of deployment are given as medians, since this parameter better reflects the majority of hospitals in our data set compared to the mean.

Of a total of 152 hospitals requested, 41 participated in the survey (response rate 27.0%). One main question and 8 sub-questions (“if yes” questions) could not be evaluated since the response rates or number of data sets were too low.

An overview of the participating hospitals in terms of geographic location, level of care and Covid-19 patient volume is shown in Table  2 .

Intensive care bed capacity

Even before the pandemic (January and February 2020), bed closures due to staff shortages in intensive care occurred in 80.5% of the hospitals surveyed (see Fig.  1 a). On median, four beds per day had to be closed.

The additional need for intensive care beds in the course of the pandemic was mainly met by activating intensive care beds previously (February 2020) considered as non-operational in existing ICUs (80.5% of the participating hospitals) and by upgrading recovery rooms (73.2% of the participating hospitals). A median of 8 intensive care beds were prepared in recovery rooms, but they were not occupied. Other measures, such as upgrading operating rooms (17.1%) and preparing external ICUs (7.3%), were taken by a comparatively lower proportion of the participating hospitals (see Fig.  1 b-c). Due to their low occurrence, there are only a few data sets on the number of prepared ICU beds, occupancy rate and duration of occupancy, which do not allow any valid statements to be made.

figure 1

Measures to expand intensive care capacities during the first wave of the COVID-19 pandemic. ( a ) Hospitals were asked whether bed closures due to critical care staffing shortages occurred prior to the pandemic in their hospital (in January and February 2020). Answers are given in percentage terms. n  = 41, N.s./n.a. Not specified/not answered. The median number of beds blocked per day was 4 (data not shown in the graph). ( b ) Hospitals were asked if intensive care beds had been activated in their hospital during the pandemic which were still considered inoperable as of February 2020. Answers are given in percentage terms. n  = 41, N.s./n.a. Not specified/not answered. ( c ) Hospitals were asked whether they were preparing to run new ICUs in recovery rooms (RR), operating rooms (OR) and outside the hospital (e.g., in mess halls). Answers are shown as percentages for each area. n  = 40–41, N.s./N.a. Not specified/not answered

Personnel situation in intensive care medicine before and during the pandemic

A median of 42 full-time positions for healthcare and nursing staff, 12 full-time positions for physicians, 1 full-time position for physiotherapists (PT)/respiratory therapists (RT) and 1.5 full-time positions for ward assistants were provided for intensive care per hospital before the pandemic (absolute numbers). After standardization, at median 1.68 full-time positions per ICU bed set up were planned for the occupational group of healthcare and nursing staff. Of these, 0.16 positions could not be filled (vacancy rate 9.5%). In the occupational group of physicians, a median of 0.4 full-time positions per ICU bed set up were included in the staffing plan, all of which could be filled (vacancy rate 0%). In the occupational groups of PT/RT and ward assistants, 0.05 full-time positions per ICU bed set up were planned, which were also filled at median (vacancy rate 0%) (see Fig.  2 a).

When asked for temporary staff, approximately one third of the hospitals that provided information on this (9/29; 31.0%) had employed temporary staff primarily in the nursing service of their ICUs (median 9.3 full-time positions). Temporary staff was employed to a much lesser extent in the PT/RT group (median 3.0 full-time positions) and in the medical service (median 1 full-time position) (data not shown).

The increased demand for intensive care beds due to the pandemic also led to an increase in the demand for nursing and medical staff. In the occupational group of healthcare and nursing staff, a median demand of 0.26 additional full-time positions per operable ICU bed (as of April 24, 2020) had developed. Regarding the occupational group of physicians, a median demand of additional 0.10 full-time positions per operable ICU bed was found. While the majority of the additional positions required in the medical service (0.07 full-time positions per operable ICU bed) could be filled by reallocating/shifting staff and by recruiting/employing new staff, only 42% of the nursing staff (0.11 full-time positions per operable ICU bed) could be done so (see Fig.  2 b). Additional demand for the staff groups of PT/RT and ward assistants could not be identified (data not shown).

In addition to increasing the number of personnel, changes of the staffing ratios were also used. Slightly more than half of the hospitals (56.1%) reported a change in the nurse-to-patient ratio or nursing-skill-mix (see Fig.  2 c). Since further data on this are partly incomplete, no more precise statements can be made with regard to how and to what extent the ratio(s) had changed.

figure 2

Staffing situation in critical care before and during the first wave of the COVID-19 pandemic. ( a ) Number of designated and vacant positions (full-time) for critical care medicine before the pandemic. Hospitals were asked to provide corresponding information on the specified professional groups in their hospital. The number of positions was related to the number of ICU beds set up (as of January 1, 2020); the medians of the hospitals are shown ( n  = 24–29). PT/RT Physiotherapists/respiratory therapists. ( b ) Additional staff requirements (full-time) in intensive care medicine during the pandemic and actual filling of the additional positions (through reallocation/shifting and through recruitment/new hires). The hospitals were asked to provide corresponding information on the specified occupational groups in their hospital. The number of positions was related to the total number of currently operable intensive care beds (as of April 24, 2020); the medians of the hospitals are shown ( n  = 26–29). No additional demand was identified for the staff groups of physiotherapists/respiratory therapists, ward assistants and others (data not shown). ( c ) The hospitals were asked whether the nurse-to-patient ratio or nursing-skill-mix of the staff in intensive care medicine had changed at their hospital. Answers are given in percentage terms. n  = 39, N.s./n.a. Not specified/not answered

Staff recruitment

When asked what measures were used to meet staffing needs during the pandemic, reallocation/shifting of staff was cited most often (85.4%), followed by requesting former staff retired or currently on parental leave (48.8%), increasing the working hours of internal staff (48.8%) and new recruits (incl. temporary contracts; 43.9%). Temporary staffing increased in 31.7% of the participating hospitals (see Fig.  3 a).

The reallocation/transfer of staff played a central role in covering the personnel requirements. Shifts of staff between different disciplines within a hospital occurred in 80.5% of participating hospitals. Moreover, 39% indicated that they had transferred staff between different ICUs within their hospital. The transfer of staff between different facilities within a group and between different facilities in a region took place only in a small proportion of hospitals (7.3%) and in none of them, respectively (see Fig.  3 b).

Furthermore, hospitals were asked which strategies and instruments they used as part of the recruitment of new staff. In addition to calls on clinic homepages (36.6%) and in other media, e.g. social media (41.5%), 36.6% of the hospitals reported that they had established an internal office to manage pandemic-related staff recruitment. The involvement of an external personnel recruiter took place in only 7% of participating hospitals (see Fig.  3 c).

Special incentives for staff recruitment were used by 17.1% of the hospitals (data not shown). Those came in form of cash benefits and/or additional leave.

figure 3

Recruitment efforts in the first wave of the COVID-19 pandemic. ( a ) Instruments and strategies used to meet staffing needs that were indicated by participating hospitals. Answers are given in percentage terms; multiple answers were possible. n  = 38, N.s./n.a. Not specified/not answered. ( b ) Types of reallocation/shifts of medical personnel in the participating hospitals. Answers are given in percentage terms; multiple answers were possible. n  = 35, N.s./n.a. Not specified/not answered, ICUs intensive care units. ( c ) Strategies and instruments used in the context of recruiting new employees that were reported by participating hospitals. Answers are given in percentage terms; multiple answers were possible. n  = 39, N.s./n.a. Not specified/not answered

Reallocation/shift of personnel

In 82.9% of hospitals, physicians from anesthesiology departments, who are normally assigned to the operating room, were appointed to the ICU (Fig.  4 a). A median of five full-time positions or five anesthesiologists were deployed over a period of about 8 weeks. In about one in two hospitals (48.9%), also physicians from other disciplines, in which intensive care medicine is part of the specialist training, were transferred to the ICU. In this case, the number of data sets was too small to make a valid statement regarding the number and corresponding duration of deployment of the respective staff members.

The reallocation of doctors without intensive care training was negated by 80.5% of participating hospitals (see Fig.  4 a).

Among nursing staff, particularly anesthesia nurses or anesthesia technicians were deployed in the ICU (85% of participating hospitals) (see Fig.  4 b). A median of five full-time positions or six staff members at maximum were assigned over a period of 6 weeks.

In about every second hospital, surgical nurses or surgical technicians (53.7%) as well as nursing staff from normal wards or intervention units (51.2%) were assigned to intensive care medicine (see. Fig.  4 b). Here again, the data sets were too small to make a valid statement regarding the number and corresponding duration of deployment of the respective staff members. The reallocation of other personnel was negated by 73.2% of participating hospitals (see Fig.  4 b).

figure 4

Reallocation of personnel to intensive care units during the first wave of the COVID-19 pandemic. ( a ) Reallocation of physicians according to their professional qualification. Hospitals were asked whether physicians of anesthesiology departments, physicians of other departments where intensive care medicine is part of the specialist training and physicians of other departments in which intensive care medicine is not part of the specialist training were deployed to ICUs in their hospital. Answers are given in percentage terms for each group. n  = 39, N.s./n.a. Not specified/not answered. ( b ) Reallocation of nursing staff according to their professional qualification and of other employees. Analogously, the hospitals were asked whether anesthesia nurses and anesthesia technicians (corresponding in Germany to Anästhesie-Fachpflegekräfte bzw. Anästhesie-Technische Assistenten (ATAs) ), surgical nurses and surgical technicians (in Germany OP-Fachpflegekräfte bzw. Operationstechnische Assistenten (OTAs) ) as well as nursing personnel from normal wards or intervention units (e.g., cardiac catheterization lab) were deployed to ICUs in their hospital. Answers are given in percentage terms for each group. n  = 39, N.s./n.a. Not specified/not answered

Evaluation of recruitment measures

Reallocations/transfers between different disciplines within a hospital and between different ICUs emerged as the most effective measures in the pandemic (Fig.  5 ). On a scale from 1 to 5, with 1 being not effective at all and 5 being very effective, both instruments were rated 4 or 5 by 53.7% (shifting between different disciplines) and 39% (shifting between different ICUs) of the participating hospitals, whereby transfer of personnel between different disciplines took place in nearly every hospital. Expanding the use of temporary staff and increasing the hours of internal staff were each rated at 4 or 5 by approximately one third of the clinics, followed by inquiring of former staff retired or currently on parental leave (29.3%), recruiting/new staff (26.8%), and using an internal position to manage pandemic-related staff recruitment (22.0%). Conventional strategies such as appeals on homepages or through other media (including social media) were rated 1 or 2 by 29.3% of participating hospitals. In comparison, only 12.2% of the clinics rated these as 4 or 5.

Other measures such as reallocation between different facilities of a group or between different facilities of a region as well as the engagement of a professional external personnel recruiting were not used by the majority of the participating hospitals, the significance with regard to their efficiency is therefore very limited.

figure 5

Assessing the effectiveness of recruitment interventions and strategies. Hospitals were asked to rate the effectiveness of various recruitment measures and strategies during the first wave of the pandemic using a scale of 1 ( not very effective at all ) to 5 ( very effective ). Data sets from 39 of the 41 participating hospitals were included in the analysis. Only those participating hospitals that provided a rating (from 1 to 5) were considered in determining the median

In this study, we assessed the staffing situation as well as local concepts for staff recruitment and their effectiveness throughout German ICUs during the first wave of the COVID-19 pandemic. The results from this nationwide analysis showed that the activation of ICU beds, which were previously considered non-operational (due to staff shortages), and the preparation of recovery rooms played a central role in expanding pandemic-related ICU bed capacity. In preparation of the expected rise in infections at the beginning of the pandemic, the federal and state governments had decided to double the number of intensive care beds by building up temporary intensive care capacity [ 24 ]. The preparation of recovery rooms to increase intensive care capacities is also listed among the recommendations on “Hospital Operational Planning and Crisis Management”, which was released by the Federal Office of Civil Protection and Disaster Assistance [ 11 , 25 ]. Also, by international standards, upgrading recovery rooms appears to be a key element in rapidly increasing intensive care capacity in times of crisis [ 26 , 27 , 28 , 29 ].

According to our data, the construction of external ICUs and the preparation of operating rooms was, in comparison, of minor importance. Only 3 out of the 41 hospitals surveyed confirmed the operation/construction of external ICUs. These hospitals represent university or tertiary care hospitals that were classified as hospitals with middle (1) and high COVID-load (2) and/or hospitals that were located in highly affected regions during the first wave of the pandemic. Only one (high COVID-load) of these three hospitals actually used their external ICU for COVID patients.

From a global perspective, the conversion of facilities to external ICUs depends on regional infrastructure and politics. While for Israel, for example, the use of external ICUs was reported through repurposing existing infrastructure, such as an underground parking lot that was otherwise used as an emergency shelter hospital in times of war [ 30 ], this would not have been conceivable in Germany. Here, for example, exhibition halls and sports halls were rebuilt in COVID-19 centers [ 31 ]. The preparation of operating rooms was a key element in enhancing ICU capacity in other countries as well. Lefrant et al. reported that 32% of new ICU beds were created by upgrading operating rooms in France [ 29 ]. Likewise, in Italy, Carenzo et al. [ 28 ] described that ICU capacity had been increased substantially by converting operating rooms.

The extension of intensive care capacity is accompanied by an increasing need for medical and nursing staff. The results of the present study show that in particular, the shortage of nursing staff, which was already present before the pandemic, exacerbated during the pandemic, despite various recruitment efforts. A similar picture emerges in other countries, e.g. Australia [ 32 ]. Despite the activation of ICU beds, the quality of intensive care treatment in many places does not meet the pre-pandemic standards. Impairments in the quality of care are due to staff shortages, high workloads, inadequate provision of protective equipment for staff, shortage of medication and ventilation equipment, as well as knowledge deficits due to the novelty of the disease and lack of experience/routines [ 33 , 34 ]. Nursing staff reported improvised conditions, situations that put patients at risk and the fear of making mistakes [ 34 ]. The enormous workload, the new and challenging working conditions and the fear of infecting oneself and loved ones such as family and friends have also led to an increased susceptibility of healthcare professionals to psychological stress, which has resulted in higher prevalence rates for anxiety, depression, burn-out, acute stress disorder and post-traumatic stress disorder [ 35 ]. According to a study by Lai et al. [ 36 ], nursing staff, women and front-line workers have a higher risk of developing psychological stress, presumably due to more intensive patient contact, a higher risk of infection and fewer opportunities for codetermination. Women are also disproportionately represented in the nursing profession. The enhanced wearing of protective equipment is of particular importance, as it is also experienced as physically very stressful [ 34 ] and described in connection with communication difficulties, a negative impact on personal performance and on physical health (e.g., exhaustion, headache, breathlessness) [ 37 ]. In the subsequent COVID waves, persistent psychological stress was identified [ 35 , 38 ]. In addition to efforts to recruit additional staff, efforts to relieve the burden on nursing staff should therefore include offers of peer psychosocial support, the implementation of team concepts to strengthen cohesion, resilience and appreciation, as well as other measures.

To cover the increased personnel requirements in the short-term, various measures and strategies were taken by German hospitals. The reallocation/shifting of personnel, primarily between different disciplines within hospitals, played a central role and was used in almost all of the participating hospitals in our survey. Further measures that were implemented, albeit less frequently, included inquiring of former employees who had already retired or were currently on parental leave, extending the working hours of internal forces, recruiting new staff (incl. short-term contracts) and expanding the share of temporary employment. For the implementation of some measures, a change or suspension of the existing legislation was necessary. The change in the nurse-to-patient ratio or the nursing-skill-mix, for example, was enabled by a temporary suspension of the PpUGV [ 9 ] and took place in every second of the participating hospitals. The aim was to enable hospitals to adjust their workflows at very short notice and to briefly relieve them of the requirements for nursing staff deployment in care-sensitive areas. Effective August 1, 2020, the regulations for critical care and geriatrics were reinstated to avoid understaffing in nursing and jeopardizing the particularly vulnerable patients to be treated in these two areas. The nurse-to-patient ratio defines the maximum number of patients per nurse, while the nursing-skill-mix represents the ratio of nursing and auxiliary staff. Until January 31, 2021, the PpUGV provided a maximum of 2.5 patients per nurse during day shifts or 3.5 patients per nurse during night shifts in intensive care [ 9 ].

The measures reported here are in line with previous reports using information from websites of Federal and State Ministries of Health and public health facilities or data from the COVID-19 Health System and Response Monitor platform [ 13 , 14 , 17 ]. These studies also list further strategies for Germany, such as the recruitment of trained foreign personnel and support by medical personnel from the military, which, however, have not been explicitly asked for in our study. Countries in the European region and Canada adopted at least two or more measures in combination [ 14 ].

With regard to the reallocation of staff, personnel from anesthesiology departments (physicians and nursing staff) have been primarily deployed in ICUs. This is mainly due to the fact that intensive care is a prominent part of training for both anesthesiologists as well as anesthesia nurses and that at the same time elective surgeries and interventions had been cancelled at a very early stage of the pandemic in Germany (with a low number of COVID patients), leading to a freeing of personnel resources. Additionally, medical staff trained in intensive care medicine from other specialties as well as surgical nurses/surgical technicians and nurses from general wards and intervention units were temporarily shifted to ICUs in one out of two participating hospitals. Reallocation, however, is not entirely unproblematic, as patients and tasks in ICUs require specific expertise, incl. handling of a variety of medical equipment, and experiences. Takeover of tasks by non-specialist nurses and physicians should therefore only be applied with appropriate training in intensive care. In Germany, working on an ICU formally requires a completed 3-year vocational training as a general nurse [ 39 ] or, equivalently, a Bachelor’s degree in nursing/nursing science. This can be supplemented by a 2-year specialist further training in “Intensive Care” or “Intensive and Anesthesia Care”. According to legal regulations [ 40 , 41 ] and recommendations by the DIVI [ 42 ], a certain minimum proportion of nurses with additional specialist further training must be available in the nursing team in ICUs on each shift.

While previous studies on expanding and securing staff capacity mainly concentrated on qualitative analyses of the measures used, our study also evaluated their efficiency in practice based on individual ratings of participating hospitals. The findings on this reflect subjective perceptions which are presumably geared more towards the professional group of physicians as 95% of the central contacts were doctors in management positions in the field of intensive care medicine (see Table  2 ). Our results uncover that the reallocation of staff between different disciplines within a hospital and between different ICUs within a hospital were rated as the most effective recruiting measures, whereby reallocation between different disciplines was used by the majority of participating hospitals. Compared to this, reallocation of staff between different ICUs occurred somewhat less, probably indicating some specialization of ICUs in the care for COVID patients. However, when comparing those findings with the data in Fig.  2 b providing a more objective assessment, it becomes apparent that the reallocation alone is not sufficient to cover the additional demand for personnel. This is particularly visible in the nursing staff group.

Measures like “expansion of temporary employment”, “increase working hours of internal staff”, “inquiry with former employees who have retired or are currently on parental leave” and “new recruitments”, however, were perceived as less effective; reasons could be administrative efforts which are linked to these but also limited availability of former and new personnel as well as limited capacities for increasing working hours. Initiatives such as appeals on websites and social media were rated least effective. Target persons may not have felt personally addressed or have not used these communication tools.

Although the establishment of an internal position to manage the pandemic-related staff recruitment was reported by over a third of the respondents, it was comparably rated as ineffective by the majority. Maybe operating and communication processes need to be optimized to make this measure more efficient and provide stronger support in recruitment and training matters for hospitals in crisis situations. The New York City (NYC) Health and Hospitals organization, for example, which operates NYC’s public hospitals, has been very successful in implementing this tool with others as part of redesigned recruitment, onboarding, and training processes, and has been able to acquire a large number of additional staffing members [ 43 ].

When evaluating the recruitment strategy in this study, it should be noted that it only considers the increase in personnel, but does not take into account the quality of intensive care. Future studies should include this point (e.g., by recording quality indicators) and involve it in the overall assessment, as this is the only way to make statements about the actual effectiveness.

Special incentives (monetary and non-monetary) did not play a role in recruiting staff for the majority of respondents. Only a small proportion of respondents affirmed the use. How much additional staff could be recruited through this or whether the hospitals that used this measure were able to generate more staff is not answered by this study.


The response rate to the survey is only 27%, despite sending reminders to the central contact persons. This fact limits the representativeness of the results. In addition, the online questionnaire was sometimes answered incompletely. As a consequence, some questions could not be evaluated due to only few available data sets. One reason for non-participation or non-response to individual questions might have been the challenge to report detailed numeric data with regard to the staffing situation and intensive care bed capacity, which might have required some internal inquiry. Additionally, the COVID-19-related tense situation in the hospitals at the time of the survey might have prevented participation in individual cases.

Furthermore, the low response rate did not allow us to run subgroup analyses and further differentiate the results between the level of care of the hospitals and the corresponding COVID burden. The selection of an initial larger sample and/or a modified implementation strategy might be useful tools for future online surveys.

Other limitations are the transferability of the results and the lack of testing validity and reliability on the questionnaire. The results should primarily be considered in the context of hospitals in Germany. Generalization or transferability to other healthcare systems is limited due to the existing differences between the healthcare systems, including different training concepts for medical and nursing staff. The choice of closed and semi-open item response options as well as precisely formulated and clearly defined questions for collecting information were intended to ensure valid and reliable data, although statistical tests on these quality criteria were not carried out in advance of the survey.

Nevertheless, compared to previous reports, the data of our study were collected directly from the hospitals themselves and thus provide not only a qualitative but also a quantitative insight into the strategies and measures used for workforce planning during the first wave of the COVID-19 pandemic in Germany. In addition, the study provides, to our knowledge, for the first time an evaluation of which of the measures were perceived effective in practice. Of equal interest would have been the recruitment processes/measures used in subsequent waves of the pandemic and resulting changes, which may be the subject of future studies.

The results of our study provide detailed insights into how hospitals in Germany managed the first Covid-19 wave with regard to the bed and staffing situation. By activating intensive care beds that previously considered inoperable due to staff shortages and preparing recovery rooms additional intensive care capacity has been made available. Furthermore, our findings reveal that the pandemic has exacerbated the existing shortage in nursing staff despite numerous recruitment efforts. This fact reflects a key issue that was and continues to be critical also in other settings.

Reallocation/shifting of staff within hospitals was a pivotal element in meeting staffing needs, although further measures are required in addition. Number and type of those employed may depend on several factors (e.g., local, structural and/or financial). Our findings provide an important and valuable decision-making aid to support healthcare providers and policymakers in preparing for and responding to future crises involving acutely increasing patient numbers and the need for rapid expansion of intensive care capacity.

Data availability

The data sets generated and analyzed during the current study are not publicly available due to reasons of data protection but are available from the corresponding author, CN, on reasonable request. Acceptance is subject to approval from participating hospitals.


Deutsche Interdisziplinäre Vereinigung für Intensiv- und Notfallmedizin , German Interdisciplinary Association for Intensive Care and Emergency Medicine

Full-Time Position

Intensive Care Unit(s)

Pflegepersonaluntergrenzenverordnung , Nursing Staff Lower Limit Ordinance


Robert Koch Institute

Respiratory Therapist

World Health Organization

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We are grateful to all participating hospitals, particularly the central contact persons who contributed to this study. We would also like to thank the RKI for providing the Intensive Care Registry data for sampling. Furthermore, we wish to thank Saša Sopka (Aachen University Hospital), Martin Klasen (Aachen University Hospital), Sophie Lambert (Aachen University Hospital) and Gunther Hempel (Leipzig University Hospital) for their input to this study.

The study was funded within the egePan Unimed project by the German Federal Ministry of Education and Research (BMBF) as part of the Netzwerk Universitätsmedizin (NUM) initiative (grant no. 01KX2021). Additional financial support was provided by the principal investigators’ institutions. The funders had no role in the design of the study, collection, analysis and interpretation of data or in writing the manuscript.

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CN, HB, BS, MSC, BB, LCS and FJ contributed to planning and realization of the study, including preparation of study material and communication with hospitals. Data evaluation and writing of the manuscript were conducted by LCS, FJ and MS. BB, HB, NE, BS and MSC critically reviewed the manuscript and made substantial contributions to it. CN scientifically supervised the study, contributed to the interpretation of data and to the drafting of the manuscript. All the authors have read and approved the final manuscript.

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Stroth, L.C., Jahns, F., Bode, B. et al. Workforce strategies during the first wave of the COVID-19 pandemic: a retrospective online survey at intensive care units in Germany. BMC Health Serv Res 24 , 407 (2024). https://doi.org/10.1186/s12913-024-10848-w

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BMC Health Services Research

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nursing research study articles

This paper is in the following e-collection/theme issue:

Published on 29.3.2024 in Vol 26 (2024)

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study

Authors of this article:

Author Orcid Image

Original Paper

  • Sumi Sung 1 , PhD   ; 
  • Youlim Kim 2 , MPH   ; 
  • Su Hwan Kim 3 , PhD   ; 
  • Hyesil Jung 4 , PhD  

1 Department of Nursing Science, Research Institute of Nursing Science, Chungbuk National University, Cheongju, Chungcheongbuk-do, Republic of Korea

2 Department of Radiation Oncology, College of Medicine, Seoul National University, Seoul, Republic of Korea

3 Department of Information Statistics, Gyeongsang National University, Jinju, Gyeongsangnam-do, Republic of Korea

4 Department of Nursing, College of Medicine, Inha University, Incheon, Republic of Korea

Corresponding Author:

Hyesil Jung, PhD

Department of Nursing

College of Medicine

Inha University

100 Inha-ro, Michuhol-gu

Incheon, 22212

Republic of Korea

Phone: 82 32 860 8206

Fax:82 32 874 5880

Email: [email protected]

Background: Few studies have used standardized nursing records with Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration.

Objective: This study aims to standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via standardized nursing records.

Methods: In this study, 57,558 nursing statements from 226 patients with COVID-19 were analyzed. Among these, 45,852 statements were from 207 patients in the stable (control) group and 11,706 from 19 patients in the exacerbated (case) group who were transferred to the intensive care unit within 7 days. The data were collected between December 2019 and June 2022. These nursing statements were standardized using the SNOMED CT International Edition released on November 30, 2022. The 260 unique nursing statements that accounted for the top 90% of 57,558 statements were selected as the mapping source and mapped into SNOMED CT concepts based on their meaning by 2 experts with more than 5 years of SNOMED CT mapping experience. To identify the main features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected for nursing problems or outcomes and nursing procedure–related statements. Additionally, logistic regression analysis was conducted to identify features that determine clinical deterioration in patients with COVID-19.

Results: All nursing statements were semantically mapped to SNOMED CT concepts for “clinical finding,” “situation with explicit context,” and “procedure” hierarchies. The interrater reliability of the mapping results was 87.7%. The most important features calculated by random forest were “oxygen saturation below reference range,” “dyspnea,” “tachypnea,” and “cough” in “clinical finding,” and “oxygen therapy,” “pulse oximetry monitoring,” “temperature taking,” “notification of physician,” and “education about isolation for infection control” in “procedure.” Among these, “dyspnea” and “inadequate food diet” in “clinical finding” increased clinical deterioration risk (dyspnea: odds ratio [OR] 5.99, 95% CI 2.25-20.29; inadequate food diet: OR 10.0, 95% CI 2.71-40.84), and “oxygen therapy” and “notification of physician” in “procedure” also increased the risk of clinical deterioration in patients with COVID-19 (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97).

Conclusions: The study used SNOMED CT to express and standardize nursing statements. Further, it revealed the importance of standardized nursing records as predictive variables for clinical deterioration in patients.


As of September 27, 2023, the World Health Organization reported more than 770 million confirmed cases of COVID-19, including approximately 6.9 million deaths [ 1 ]. In South Korea, among 34,436,586 confirmed cases between January 3, 2020, and August 31, 2023, there were 35,812 deaths attributed to COVID-19 [ 2 ]. Among them, older patients or those with underlying diseases or comorbidities died due to severe conditions during the beginning of COVID-19; however, other cases exhibited initially mild symptoms that gradually worsened, causing death. Therefore, early detection of aggravating factors and symptoms is crucial for timely intervention and treatment.

COVID-19 symptoms include fever, cough, sore throat, nasal congestion, malaise, headache or muscle pain, dehydration, pneumonia, sepsis, and shortness of breath [ 3 ]. COVID-19 mortality prediction relies on patient age, blood oxygen saturation, and body temperature [ 4 ]. Through a systematic evaluation and external validation of 22 prognostic models for COVID-19 [ 5 ], admission oxygen saturation on room air and age have been shown to be strong predictors of clinical deterioration and mortality, respectively, in adults hospitalized with COVID-19.

The electronic health records (EHR) system contains detailed information about symptoms, problems, and services or care provided to patients with COVID-19. Previous studies have used EHR data to predict the COVID-19 prognosis [ 6 - 8 ]. However, nursing documentation records, which account for a substantial portion of EHR data, are underused for research owing to their low quality of documentation [ 9 , 10 ]. Unlike other EHR data, nursing documentation lacks standardization than other data such as diagnoses, laboratory, and medication data. The common data model (CDM) of the Observational Medical Outcome Partnership (OMOP) is designed to standardize the structure and content of observational data from multiple sites for efficient analyses that can produce reliable evidence; it is rarely used to standardize nursing documentation. One study [ 11 ] standardized 6277 nursing statements data using Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) and converted them into the OMOP CDM to develop a fall-prediction model along with other CDM data. SNOMED CT is the most comprehensive multilingual clinical terminology [ 12 , 13 ] used by more than 40 countries and 30,000 individuals or organizations. The SNOMED CT International Edition, published on April 30, 2023, includes 360,942 concepts, 1,595,980 descriptions (synonyms) of concepts, and 3,261,032 relationships between concepts. Despite these advancements, there is a lack of empirical studies analyzing patient outcomes using standardized nursing documentation or records with SNOMED CT.

The American Nurses Association in 2015 and 2018 position statements also recommended the use of SNOMED CT to code nursing assessments or outcomes to facilitate the interoperability of nursing data. Several studies have examined the content coverage of SNOMED CT in the nursing domain by mapping standardized nursing terminologies, such as the Nursing Intervention Classification, International Classification for Nursing Practice (ICNP), Clinical Care Classification System, North American Nursing Diagnosis Association-International, and Omaha system, into SNOMED CT [ 14 - 18 ]. Thoroddsen et al [ 19 ] described the nursing care of patients with COVID-19 using ICNP and SNOMED CT. SNOMED CT outperformed ICNP in representing COVID-19 diagnoses and interventions. In particular, SNOMED CT comprehensively covered nursing interventions for patients with COVID-19. However, prior research only evaluated its usability or content coverage in the nursing domain, with limited analysis of specific phenomena or problems. Therefore, analyzing specific phenomena or problems using standardized nursing documentation data with SNOMED CT is necessary.

This study aimed to (1) standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and (2) identify predictive factors of clinical deterioration in patients with COVID-19 using standardized nursing records.

Study Design and Setting

This retrospective observational study used data extracted from a clinical data warehouse (CDW) at a tertiary hospital with 1782 beds in Seoul, South Korea. The hospital operated 2 general wards and 4 disaster intensive care units (DICU) to treat COVID-19.

In this study, the clinical outcome is the patient’s clinical deterioration. We defined clinical deterioration as an event in which patients with COVID-19 were transferred to the ICU from general wards.

Study Participants

In total, 460 patients with COVID-19 were admitted to 2 general wards for infectious disease treatment at the hospital between December 2019 and June 2022. Patients transferred from or to other general wards or ICUs other than the DICU were excluded. Among the 460 patients, 19 patients were transferred to the DICU because of their deteriorating clinical condition. The mean length of stay (LOS) in the general ward of the 441 patients who were not transferred to the DICU during hospitalization was 8.71 days. The mean and maximum LOS of the 19 patients transferred to the DICU were 3.11 and 7 days, respectively.

Considering LOS differences between the 2 groups, the final analysis of the study included 226 admitted patients with COVID-19 at 2 general wards who were discharged or transferred to the DICU within 7 days between December 2019 and June 2022. Among these, 207 patients who were discharged in a stable clinical condition from the hospital within 7 days were assigned to the control group. A total of 19 patients who were transferred to the DICU within 7 days because of their deteriorating clinical conditions were assigned to the case group.

Data Sources

Using CDW at a study hospital, the general characteristics of patients including age, gender, and initial respiratory symptoms were extracted from initial nursing assessment records. The patient’s acuity level and nursing statements were extracted from the nursing records. A total of 57,558 nursing statements from 226 patients were extracted. Among these, 45,852 nursing statements were obtained from the nursing records of 207 patients in the control group. Of the remaining records, 11,706 nursing statements were obtained from 19 patients in the case group.

Mapping to International Standard Terminology, SNOMED CT

Nursing statements were standardized by mapping them to SNOMED CT concepts. The 57,558 nursing statements consisted of 1776 unique nursing statements which were interface terminologies developed by Seoul National University Hospital. Among the 1776 unique nursing statements, 260 unique nursing statements which were recorded 50,867 in total (cumulative recorded rate of 90%) were selected as the mapping source. Among these 260 unique statements, the control group used 237 nursing statements, and the case group used 204 nursing statements.

According to the mapping guide in the previous study [ 20 ], 2 experts with more than 5 years of SNOMED CT mapping experience performed the mapping. The scope of the map was defined to restrict mapping to the precoordinated SNOMED CT concept only. Korean nursing statements were translated into English, and search terms were chosen considering the clinical context. SNOMED CT concepts are organized into 19 top-level concepts or hierarchies under the root concept. Among them, the Clinical finding (finding) hierarchy contains concepts related to symptoms and disorders. The procedure hierarchy contains concepts related to activities performed in the provision of health care and regime or therapy. The Situation with explicit context hierarchy includes concepts that specifically define the context information of a clinical finding or procedure [ 21 ]. Therefore, the nursing statements in nursing problems or outcomes and diagnosis domains were mapped to concepts of “clinical finding” and “situation with explicit context” hierarchies, and those in the nursing interventions domain were mapped to concepts of “procedure” hierarchy within the SNOMED CT International edition, released on November 30, 2022. If a SNOMED CT concept semantically consistent with the nursing statement could not be found, it was mapped to broader precoordinated concepts.

The mapping results were classified according to the level of correspondence as 3 types: “exact map,” “narrow to broad map,” and “no map.” Mappings were categorized as follows: “exact map” when the meaning of the nursing statement matched an equivalent SNOMED CT concept, “narrow to broad map” when the meaning of the nursing statement matched a broader SNOMED CT concept, and “no map” when there was no broad match concept for the meaning of the nursing statement.

Internal validation of the final map was conducted by calculating interrater reliability. Two experts reviewed the mapping results. If the mapper and reviewer selected the same result, the map was deemed correct. If the maps differed, the results were evaluated via a group discussion, and one of them was selected.

Statistical Analyses

The general characteristics of patients (eg, gender, age, and patient acuity level) in both the case and control groups were explored. For patients in the case group transferred to the DICU (average ward LOS, 3.1 days), nursing statements documented up to the third day were extracted. The total number of nursing statements was divided by the LOS to calculate the mean number of nursing statements per day. Thereafter, the data set was separated according to the top-level hierarchies of SNOMED CT mapped to nursing statements, “clinical finding” or “situation with explicit context,” and “procedure.” Among the data set, we excluded SNOMED CT concepts that do not describe the patient’s clinical problems, such as no breathlessness, free of symptoms, no sputum, and no cough. To identify key features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected with 2 data sets (“clinical finding” or “situation with explicit context” and “procedure”).

To assess the effects of the features identified by random forest algorithms on the exacerbation of patient conditions, we performed logistic regression. Patient DICU transfer status was the dependent variable, and the top 5 features from each hierarchy identified by random forest algorithms were independent variables at a significance level of α=.05. Considering the small sample size for binary logistic regression analysis, the model was fitted using a modified estimation procedure known as Firth correction [ 22 ]. R (version 4.2.2; R Foundation for Statistical Computing) was used for all analyses.

Ethical Considerations

This study was approved by the Institutional Review Board of the Seoul National University Hospital (H-2207-097-1341). The requirement for informed consent was waived according to the relevant guidelines and regulations of the institutional review board. Identifiers, such as the patient’s ID and name, were encrypted so that individuals could not be identified during the data analysis. Participant compensation was not offered since this study was a retrospective observational study.

SNOMED CT Mapping Results

The results of mapping nursing statements to SNOMED CT are presented in Table 1 . The interrater reliability of the mapping results was excellent (87.7%). Of the 260 unique nursing statements, 157 were on nursing problems or outcomes, 16 were on nursing diagnoses, and 87 were on nursing intervention statements. A total of 138 (87.8%) of nursing problems or outcomes statements were mapped to concepts within “clinical finding” and “situation with explicit context” hierarchies on SNOMED CT. Through a validation process, 19 nursing problems and outcome statements were mapped into concepts within the procedure hierarchy. All 16 nursing diagnosis statements were mapped to clinical finding concepts. Among the 87 nursing intervention statements, 85 (97.7%) nursing intervention statements were mapped to concepts within the procedure hierarchy. Only 2 statements were mapped to the clinical finding concepts, “267038008 |Edema|” and “29658002 |Oxygen supply absent|.” Of the 260 nursing statements, 244 (93.8%) nursing statements were classified as “exact map” and 16 (6.2%) nursing statements were classified as “broad map.” The mapped concepts with high frequency are presented in Multimedia Appendix 1 .

a The number of concepts was calculated after removing duplicated concepts.

Clinical Characteristics

The clinical characteristics of the study participants are presented in Table 2 . The mean age was 55.9 (SD 17.3) years, and the mean LOS was 5.3 (SD 2.0) days. Patients in the control group were hospitalized in the ward for approximately 5.04 (SD 1.6) days. Patients in the case group were admitted to the ward for approximately 3.1 (SD 1.9) days. Overall, 119 (52.7%) patients were male. A total of 197 (86.7%) patients had a patient acuity level of 4, and 26 (11.5%) patients had a patient acuity level of 3. Additionally, 59 (26.1%) patients had a cough, 47 (20.8%) patients had sputum, and 26 (11.5%) patients had dyspnea as their initial symptoms.

b Chi-square.

c LOS: length of stay.

d DICU: disaster intensive care units.

e N/A: not applicable.

Feature Selection via Random Forest

The feature importance calculated using the random forest method is shown in Figure 1 . The most important feature in “clinical finding” showed “449171008 |Oxygen saturation below reference range (finding)|.” Prominent concepts that express respiratory issues included “267036007 |Dyspnea (finding)|,” “271823003 |Tachypnea (finding)|,” and “49727002 |Cough (finding)|.”

nursing research study articles

Among procedure concepts, “57485005 |Oxygen therapy|” was the most important feature, followed by “284034009 |Pulse oximetry monitoring|,” “56342008 |Temperature taking (procedure)|,” “428426009 |Notification of physician|,” and “737612005 |Education about isolation for infection control|.”

Association Between Patient’s DICU Transfer and Selected Features

Table 3 presents the results of logistic regression analysis, which explored the association between patient DICU transfer status and features in both “clinical finding” or “situation with explicit context” and “procedure” selected via random forest. In “clinical finding,” patients with dyspnea and inadequate food diet were more likely to experience DICU transfer (dyspnea: odds ratio [OR] 5.99, 95% CI 2.25-20.29; inadequate food diet: OR 10.0, 95% CI 2.71-40.84). Patients with infectious disease in nursing records were less likely to be associated with the DICU transfer in patients with COVID-19 (OR 0.32, 95% CI 0.16-0.66). In the procedure, patients with “oxygen therapy” and “notification of physician” were more likely to experience DICU transfer (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97). Patients with “temperature taking” were less likely to experience DICU transfer (OR 0.36, 95% CI 0.18-0.70).

a SNOMED CT: Systematized Nomenclature of Medicine-Clinical Terms.

Principal Results

This study standardized the nursing statements of patients with COVID-19 in a hospital in South Korea by mapping them to SNOMED CT, an international standard terminology. Using these standardized statements, machine learning analysis with a random forest was conducted. Concepts that could predict patient deterioration in nursing problems or outcomes and intervention domains were identified. In the nursing problems or outcomes domain, concepts in nursing statements related to dyspnea, tachypnea, and oxygen saturation below the reference range were associated with patient deterioration. In nursing interventions, concepts in nursing statements related to respiratory intervention, including oxygen therapy, pulse oximetry monitoring, and physician notification, were associated with DICU transfer. These concepts were different from the findings of another study that analyzed nursing statements of patients with ovarian cancer. Kim et al [ 23 ] analyzed standardized nursing statements of patients who underwent curative surgery for epithelial ovarian cancer, and urination, food supply, bowel mobility, and pain were identified as the most common concepts.

To the best of our knowledge, this study is the first to identify predictive factors from nursing records standardized using SNOMED CT. Although previous studies [ 24 - 29 ] have used standardized nursing records such as the North American Nursing Diagnosis Association-International, Nursing Intervention Classification, Nursing Outcomes Classification, and ICNP to predict the patient’s condition or explore the nursing care provided, no study has predicted patient clinical deterioration using SNOMED CT–standardized nursing records. To achieve interoperable health information exchange, the Centers for Medicare and Medicaid Services and Office of the National Coordinator for Health Information Technology recommended the use of SNOMED CT and Logical Observation Identifier Names and Codes (LOINC) as reference terminologies. In 2020, the Office of the National Coordinator for Health Information Technology introduced the United States Core Data for Interoperability, a standardized set of health data classes and constituent data elements that recommended the use of international standard terminologies, including SNOMED CT and LOINC [ 30 ]. Consequently, standardization of nursing records using reference terminology before conducting data analysis is required in the nursing field.

Our study comprehensively standardized all nursing statements in study participants using SNOMED CT concepts. Several previous studies have evaluated SNOMED CT coverage in various health care domains, such as frailty and wound care [ 31 - 34 ]. In nursing, efforts have been made to map ICNP 7-axis concepts, nursing problem lists, and ICU nursing flowsheets to SNOMED CT [ 16 , 32 , 35 - 37 ], which provides the most detailed semantic expression compared with other standard terminologies [ 19 ]. Notably, this study unexpectedly mapped 19 nursing statements on nursing assessments or outcomes to the “procedure” hierarchy, despite the lack of corresponding concepts in the “clinical finding” or “situation with explicit context” hierarchies. This was due to the fact that the problem or phenomenon was the result of specific nursing procedures or practices. For example, the statement “a patient is applying a mattress to prevent bedsores” in nursing assessment or outcome was mapped to concept “733920005 |Provision of pressure-relieving mattress|” in the procedure hierarchy.

The identified features affecting patient deterioration (DICU transfer) in the hierarchy of “clinical finding” included respiratory issues such as low oxygen saturation, dyspnea, and tachypnea. Izquierdo et al [ 7 ] reported patient signs and symptoms, especially tachypnea, to be reliable predictors of DICU admission, which is consistent with our results. In addition, inadequate diet was significantly associated with DICU transfer, potentially due to the inability of patients to eat independently owing to their deteriorated condition. These findings highlighted the importance of signs and symptoms in nursing records for predicting a patient’s deteriorating clinical condition. Alternatively, nursing statements related to “infectious disease” within “clinical finding” and “temperature taking” within “procedure” decreased the risk of clinical deterioration in patients with COVID-19. Since patients with COVID-19 were included as study participants, it was valid that the concept of “infectious disease” appeared frequently in nursing statements, but repeated studies are needed to explain the finding that the more statements related to this concept, the lower the risk of clinical deterioration. Considering previous studies [ 4 , 38 ] reporting “fever” as a significant factor affecting deterioration in patients with COVID-19, our study revealed that increased nursing activity of “temperature taking” notably reduced patient deterioration risk. This suggested that nurses may have preemptively frequently monitored temperature to prevent fever; however, to accurately interpret this result, analyzing “body temperature” data is necessary. The features identified in this study highlight the potential of nursing records as a valuable real time predictor of a patient’s clinical condition.


This study had some limitations. First, the analyzed data were extracted from a single tertiary university hospital, which limits the generalizability of this study. Second, sample bias is possible because patients admitted for more than 7 days to the ward were excluded. Third, other clinical findings or nursing interventions may not have been documented as nursing statements in the nursing records. Considering that this study extracted only structured nursing statements stored in the CDW, records written in free text were not included in the analysis. Fourth, as SNOMED CT concepts were used in the analysis, the results may vary depending on the hierarchical structure or level of the mapped SNOMED CT concept. For example, concepts such as “chest pain,” “headache,” and “pain” derived through random forest are connected in a hierarchical structure where “chest pain” and “headache” are subcategories of the “pain” concept. If “chest pain” and “headache” were grouped together as “pain,” the effect of “pain” on clinical deterioration might have been significant.


This study showed that standardized nursing records are an important source of data that can be used to predict clinical deterioration in patients with COVID-19. In total, 260 nursing statements were mapped to the SNOMED CT, including 109 concepts in the clinical finding hierarchy, 73 concepts in the procedure hierarchy, and 22 concepts in “situation” with an explicit context hierarchy. Among the standardized nursing statements, key clinical findings were respiratory issues, including low oxygen saturation, dyspnea, and tachypnea. The primary procedure-related features included oxygen therapy, pulse oximetry, and temperature monitoring. In specific, low oxygen saturation, dyspnea, tachypnea, and oxygen therapy are associated with the risk of clinical deterioration in patients with COVID-19. This study validates the use of nursing records as variables for predicting the deterioration of patients with COVID-19. Future research should investigate the integration of standardized nursing records with diagnoses, laboratory, and medication data to develop a highly reliable predictive model.


This study was supported by an Institute of Information and Communications Technology Planning and Evaluation grant funded by the Korean government (MSIT; 2021-0-00312, “Development of Non-Face-to-face Patient Infection Activity Prediction and Protection Management SW Technology at Home and Community Treatment Centers for Effective Response to Infectious Disease”) and the National Research Foundation of Korea funded by the Ministry of Science and Information Communication Technology (NRF-2021R1A2C1091261).

Data Availability

The data analyzed during this study are not readily available because of hospital regulation restrictions and patient privacy concerns. Requests to access the data sets should be directed to the corresponding author.

Authors' Contributions

SS and HJ conceptualized the study and reviewed and edited the manuscript. SS, SHK, and HJ developed the study methodology and drafted the manuscript. SS, SHK, YK, and HJ conducted formal analyses. HJ supervised the study and acquired funding. All the authors reviewed the results and approved the final version of the manuscript. Generative AI was not used in any part of the manuscript writing.

Conflicts of Interest

None declared.

The mapped concepts with high frequency.

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Edited by T de Azevedo Cardoso; submitted 03.10.23; peer-reviewed by J Lee, N Hardiker, D Liu; comments to author 19.10.23; revised version received 26.10.23; accepted 27.02.24; published 29.03.24.

©Sumi Sung, Youlim Kim, Su Hwan Kim, Hyesil Jung. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

March 26, 2024

The hidden dangers of secondhand vape exposure

More than half (11 of 19) of parents who participated in a focus group expressed that they believed using e-cigarettes near their children posed little or no health risk.

— Getty Images

In homes across America, an invisible threat may be lurking — one that could affect the health of our youngest and most vulnerable. Recent findings presented this month at the National Association of Pediatric Nurse Practitioners conference in Denver shed light on the often-overlooked danger of secondhand e-cigarette vapor, particularly its impact on children.

For years, electronic cigarettes have been marketed as a safer alternative to traditional smoking. But as these devices gain popularity among adults, there is growing concern over the unintended consequences for children exposed to the exhaled vapors. A new study by researchers at Emory University’s Nell Hodgson Woodruff School of Nursing and Rollins School of Public Health shows that children living in households where e-cigarettes are used are unintentionally inhaling substances that could harm their developing bodies.

The study implemented a unique approach to data collection that combined the traditional use of blood tests with less invasive saliva and exhaled breath tests to determine the exposure of children to hazardous substances. The results were telling: Children aged 4-12 years who were exposed to secondhand e-cigarette vapor showed significantly higher levels of metabolites linked to chemicals found in e-cigarette liquids compared to their unexposed peers.

These metabolites interfere with the body’s normal operations by disrupting dopamine levels and causing inflammation and oxidative stress. Oxidative stress leads to cellular damage throughout the body and is linked to numerous diseases, including diabetes, heart disease and cancer.

“Many people who smoke have switched to using e-cigarettes, thinking it’s safer for them and others nearby,” says Jeannie Rodriguez, PdD, RN, associate professor at Emory’s School of Nursing and lead author of the study. “However, there are chemicals in the liquids used in a vape that are hazardous for you and those that you care about who are exposed to the vapors you exhale.”

The outcomes of the analyses were shared with parents to highlight the risks linked to exposing children to the byproducts of e-cigarette vapor.

Surprisingly, many parents were unaware of the risks. In focus group discussions with Emory researchers, more than half (11 of 19) of parents revealed they considered vaping around their children a minor concern, if a concern at all. This alarming disconnect underscores the need for education on the subject.

Health experts like Rodriguez stress the importance of equipping parents with the knowledge to make informed decisions. By understanding the tangible evidence of harm, parents might be more inclined to put down their vaping devices for good.

However, quitting is not straightforward. The study also unveiled that despite understanding some risks, the addictive grip of nicotine and the belief that vaping is less harmful than traditional smoking complicate parents’ decision to stop. This highlights the nuanced challenges in combatting the vaping epidemic and the critical role health care professionals play in guiding families toward healthier choices.

“If you do vape and are ready to quit, talk to your health care provider and your family and friends,” says Rodriguez. “You may need the support of those around you to be successful. Think of past attempts to quit not as failures, but as training opportunities for you to eventually successfully quit. Don’t give up.”

As vaping continues to cast a shadow over public health, especially among the youth, the findings from this study are clear: While the vapor from electronic cigarettes may be invisible after it disperses through the air, the effects on children are not.

Coauthors of the study include  Jeannie Rodriguez , PdD, RN, and Irene Yang , PdD, RN, of Emory’s Nell Hodgson Woodruff School of Nursing and  Donghai Liang , PhD, of Emory’s Rollins School of Public Health.

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Reasons for resistance to change in nursing: an integrative review

Rozita cheraghi.

1 Ph.D. Candidate in Nursing. Student Research Committee, Tabriz University of medical sciences, Tabriz, Iran

Hossein Ebrahimi

2 Department of Psychology Nursing, School of Nursing and Midwifery, Tabriz University of Medical Sciences, Tabriz, Iran

Nasrin Kheibar

Mohammad hasan sahebihagh.

3 Community Health Nursing Department, School of Nursing and Midwifery, Tabriz University of medical sciences, Tabriz, Iran

Associated Data

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. All requests relating to data should be addressed to [email protected].

Change is a very complex and multifaceted phenomenon that is intertwined with the understanding of nursing practice, so, resistance to change in nursing can be considered as an important challenge. Knowing the reasons for this resistance can help in solving it in nursing. Therefore, the present study was conducted with the aim of investigating the reasons for resistance to change in nursing as an integrated review.

This integrative review was conducted using the Whittemore & Knafl method in 5 stages, including problem identification, searching the literature, evaluating primary sources, analyzing data, and presenting the results. Databases like SID, Irandoc, Magiran, Google Scholar, Web of Science, PubMed, CINAHL, and Scopus were searched using the keywords; “Resistance”, “Change”, “Nursing”, “Resistance to Change” and their Persian equivalents in the time range of 2000 to January 2023. After applying inclusion criteria and assessing the articles using Bowling’s Quality Assessment Tool, finally, 15 papers were included from 2964.

After reviewing and critically appraisal of the qualified articles, the findings were placed in three main categories including; (1) individual factors, (2) interpersonal factors, and (3) organizational factors and six subcategories.

Undoubtedly, change is an integral component in nursing care, and resistance to it is the result of a set of individual, interpersonal and organizational factors that change managers should pay special attention to in order to make changes due to the reasons of this resistance, and the development process of developing changes in the clinical field is easily possible.


Change is a very complex and multifaceted phenomenon that is intertwined with the understanding of nursing practice [ 1 , 2 ], and it’s happening fast in health care, so, all nurses, as a part of the change process, must be knowledgeable and skilled [ 3 ]. In the dynamic environment of healthcare, the organization’s agility to change is the key to its survival [ 4 ]. However, not all employees in an organization react equally to ongoing changes in their workplace organization, some employees respond to these changes with enthusiasm and as opportunities for learning and growth, while others resist such changes and show increasing feelings of frustration, alienation, and sadness [ 5 ]. It would be said; a key barrier to implementing change is employee resistance to change [ 6 ]. So that, resistance to change (RtC) is widely recognized as the main reason for failure when it comes to change initiatives. Despite the importance of this issue; still relatively limited knowledge exist about the factors that cause resistance to change in the organization [ 7 ].

Generally defining the concept of “resistance to change” is not easy [ 8 ], but based on the literature; resistance is defined as the informal and covert behavior of an individual in response to a perceived or actual threat to maintain the status quo [ 9 , 10 ]. In other words, resistance is defined as failure to do anything that is asked by managers from employees [ 11 ]. Also, behavioral resistance is known as a prevent or stop change [ 9 ], which can ultimately be the main cause of change failure [ 12 ]. However, sometimes the nature of resistance can finally be a valuable resource for achieving change [ 8 , 13 ].

Organizational resistance can be caused by power and conflict, or be the result of differences in functional orientation, structure, and organizational culture [ 14 ]. Some of the reasons for the organizational changes according to the studies are restructuring in the workplace, advances in technology, a greater need for efficiency, and the growth of new services [ 3 ]. Some others at the group level include resistance to change caused by group norms, group cohesion, group thinking, and intensifying commitment [ 15 ], and at the individual level include uncertainty and Lack of job security, selective perception and retention, and getting used to the current work [ 16 ].

Implementing change in the healthcare system is difficult, challenging, and often has short-term results [ 17 ], especially when the context of change includes changes in care organization, modification of common clinical practices, increased collaboration between different disciplines, and changes in patient behavior [ 18 ]. This happens because the healthcare services are delivered in an environment where groups of people act in different and unpredictable ways, where tensions arise through opposing, competing, or collaborative forces, and where decisions are influenced by priorities, and records of healthcare professionals are adopted [ 19 ].

Studies show that; Nurses are inherently resistant to clinical change [ 1 ], and there are several reasons for this. RtC in nursing is likely based on fear, uncertainty, doubt, frustration, distrust, confusion, and anger [ 20 ]. Although accepting change is challenging for nurses, resistance is usually an ordinary and predictable reaction to change [ 21 ].

Resistance has historically been viewed with negative consequences due to its potential impact on organizational success [ 9 ]. However, resistance is a normal response to a threat to the status quo because change requires people to abandon their current processes [ 22 ]. Individual’s resistance can be an obstacle to implementing change [ 23 ], and plays an important role in successful adaptation to change [ 22 ]. Improvements in the changes in the provision of organizational healthcare are often positive and carried out to improve the quality, safety, and efficiency of healthcare, thus increasing the experiences for patients and employees. However, despite these positive results, nurses often face resistance to change and are considered a natural consequence [ 9 ].

Accepting change in the core of nursing and health care is considered a challenge, and some of these challenges are related to the movement of information and knowledge from research to the implementation of evidence-based best practices [ 24 ]. It is because employees and organizations simply do not like change [ 25 ], and the organizational culture (context and environment of the organization) that is conservative and may strengthen the resistance that can prevent the implementation of new changes [ 26 ]. This kind of resistance is the result of the cognitive and behavioral reactions of the recipients of the change towards the change [ 27 ], which is often in conflict with the organizational identity and causes an unpleasant image of individual, and threats the organizational identity [ 28 ]. Although the effect of resistance to change is not static: instead, it can have a negative, festering effect on relationships with perceived organizational effectiveness and commitment to the organization over time [ 5 ].

What is obvious is that resistance to change in nursing care can be an important challenge, although various studies have addressed the concept of change, however, very few and scattered studies have focused on the reasons for resistance in nursing. Therefore, this study was conducted with the aim of an integrated overview of the reasons for resistance to change in nurses. An integrative review is a specialized review method that summarizes empirical or theoretical studies that have already been conducted to provide a more comprehensive understanding of a specific phenomenon or healthcare problem [ 29 ]. In fact, integrated reviews have the potential to expand the body of knowledge and create nursing science, knowledge of research, practice, and policies, at the same time, this category of studies shows the current state of knowledge in each field, helps to develop theory and has a direct application in practice and health policies [ 30 ]. Therefore, the results of this study can help to clarify the reasons for resistance to change in nursing and, as a result, to solve it.

Study design

This study is an integrated review based on articles related to the reasons for resistance to change in nursing which was conducted to collect data from various studies. This integrative review was conducted using the Whittemore & Knafl method in 5 stages of review, including (a) problem identification, (b) searching the literature, (c) evaluating data from primary sources, (d) analyzing data, and (e) presenting the results, using of this method also increases the rigor of this study [ 30 – 32 ].

Search strategy

Based on the Whittemore & Knafl method, 1) in the first stage, the following question was set to answer the study’s aim: What are “the reasons for resistance to change in nursing”?

2) In the second stage, searching for articles was conducted by two researchers in the time range from 2000 to January 2023. We searched databases such as; Persian database(Magiran, SID, Irandoc), Google Scholar, Web of Science, PubMed, CINAHL, and Scopus by using the keywords; “Resistance”, “Change”, “Nursing”, and “Resistance to Change” in English and Persian separately or combined by using the Boolean operators(AND and OR). In this stage; the results of the comprehensive search included 2964 articles after reviewing them based on the inclusion criteria such as: accessing the full text of the article, including the keywords in the title and abstract of the article, and writing in Persian and English language, finally 2949 were removed, and the 15 articles were included.

Eligibility criteria

3) In this stage, two researchers evaluated the data and the content of selected studies for their quality by using “Bowling’s Quality Assessment Tool“(consists of items for checking the structure of the methodology and presenting the results of the studies: high, moderate, and low-quality) [ 33 ], which caused the removal of 5 of these articles by that. Then we compared the results, and finally 15 articles were included in this study (Fig.  1 following the renewed PRISMA guideline 2020) [ 34 ].

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PRISMA flow diagram

Data extraction

The data extraction was based on the main data from the 15 articles included: the publication’s year, the language of the study, the main keywords and the methodology of the study, and also the results.

Data synthesis

4) In the fourth stage, data were evaluated independently by two researchers and updated in a continuous process, after that, the data were analyzed and interpreted. This process was verified by three authors including; documenting the required data through five methodological stages, and analyzing separately by researchers.

All of the extracted data were read by researchers and determined significant items and similar and different data were assessed and examined throughout the data analysis process.

Eventually, confirmation and verification were performed by all authors to ensure that all 15 reviews were thoroughly evaluated in all of the methodological stages and the results were matched by the research questions of the study.

Quality appraisal

Whittemore and Knafl (2005) state that assessing the quality of the included evidence is not essential in a supplementary review [ 31 ]. All studies meeting the inclusion criteria, regardless of their methodological quality, were retained in the review to examine all evidence of the factors that influenced the nursing role implementation in practice settings. Also, Bowling’s quality checklist was used to appraise the articles, which allowed us to evaluate and compare study objectives, design, methods, analysis, results, discussion, and clinical implications [ 33 ].

For our review, studies were deemed to be of relatively high quality, and studies that were in the moderate or low-quality range were omitted.

5) In the last step, the results were obtained according to the framework of 15 articles that were selected. It is necessary to mention that was found no paper in Persian, and all articles included in this study were in English (Table  1 ).

Selective qualified articles

After reviewing and evaluating the qualified articles, the findings were classified into three main categories as follows: (1) individual factors, (2) interpersonal factors, and (3) organizational factors and six subcategories (Table  2 ).

Main categories and sub-categories extract from the review of selected articles

The present study was conducted to investigate the reasons for resistance to change in nursing as an integrated review of various studies. In this review; three main categories (individual factors, interpersonal factors, and organizational factors), six sub-categories, and thirty-seven codes were identified.

What is clear to us is that change in improving patient outcomes is common and important in the current healthcare systems [ 40 ]. The process of change is an inevitable issue in healthcare, so understanding the benefits of change for patients is most likely to be successful when caregivers have the opportunity to influence change. Making changes can be challenging because they conflict with basic human needs for a sustainable environment [ 41 ]. Although changes in clinical environments are inevitable, resistance to them for various reasons; can be created.

Based on the findings of the present study, individual factors ; It is one of the factors that can be based on the individual attitude and understanding and personality characteristics of nurses. Attitudes toward an impending change may be positive, negative, or neutral. Resistance to change in nursing is probably based on negative and defensive feelings toward change such as fear, uncertainty, doubt, disappointment, mistrust, confusion, and anger [ 42 ]. The findings of Amarantou’s study (2018) also confirm that resistance to change is indirectly influenced by individual’s emotions and personality characteristics [ 7 ]. Tendency to pessimism in employees is one of the personality characteristics that causes negative attitudes and perceptions toward change. The use of this defense mechanism is adopted unintentionally when danger occurs in order to reduce stress. The tendency to pessimism is directly related to a person’s personality and reflects a negative perception of human behavior and is characterized by pessimistic behavior and the inability to establish appropriate interpersonal relationships [ 43 ]. In the current study, individual personality characteristics indicate how pessimistic employees are toward change. Persons who have high levels of the above personality traits are more likely to experience negative emotional reactions, deny changes, show a judgmental and negative attitude towards change, and believe that the effect of implemented changes will be unfavorable [ 7 ].

All personnel in an organization does not react equally to ongoing changes in their organization [ 37 ]. A feeling of insecurity, doubt, and on the other hand, low motivation in implementing change [ 36 ], with a lack of trust and negative belief in change [ 26 , 27 ], and a lack of readiness to accept change [ 35 ] seeks resistance to change. Individuals with conservative personality traits and low flexibility to change can also make this process more difficult [ 26 ]. Changes in the structure or design of organizations as a result of the introduction of new technologies are likely to lead to changes in work roles and increased feelings of uncertainty and insecurity among personnel. Job insecurity may cause personals to resist proposed changes [ 44 ]. When personnel are satisfied with their current position in the organization, they may become increasingly anxious about future changes because they fear that intrinsic rewards and well-being will be negatively affected. Consequently, when individuals feel that their well-being is threatened, they try to protect it and resist possible changes [ 45 ].

Among other effective factors that can cause RtC in nursing is interpersonal factors of employees. Studies show that communication barriers in the organization ultimately affect the implementation, quality, and sustainability of change [ 9 ]. Employees’ job perception includes rewards and inner satisfaction that they receive from their jobs and interactions with their colleagues [ 46 ], and the positive Colleagues’ opinion are indirectly effective in the resistance behavior of personnel and reducing resistance to change [ 47 ]. The quality of communication between employees is related both to the information before the implementation of the change and to the quality of the information during the implementation of the change [ 48 ]. This factor also refers to the overall quality of communication within the organization, and studies also indicate that poor communication is related to uncertainty in change and often magnifies the negative aspects of change and creates resistance to it. Also, inadequate cross-functional and vertical communication during the stage of change implementation, makes personnel more reluctant to the proposed changes, since they are less informed. So, communication quality will be negatively associated with attitude towards change, disposition towards change and anticipated impact of change [ 7 , 49 ].

The third factor of resistance to change in nursing, is the organizational factors which are in three sub-categories; management factors, organizational values, and structural factors are placed. As mentioned, accepting change at the core of nursing and health care is a challenge because nurses are not only inflexible but also adept at strengthening the existing [ 24 ]. Therefore, changes in healthcare environments require effective managers who can implement change strategies to improve patient outcomes [ 40 ].

The effect of RtC can strengthen the negative effect on organizational effectiveness and organizational commitment, and the lack of leadership support will amplify with time. In this regard, it seems that managers supporting change in the organization can play an important role in improving resistance [ 5 ]. The results of the studies show that a key obstacle to the implementation of change is the culture reported by managers to change [ 6 ], that lack of proper education and guidance is one of the reasons for this [ 35 ], so it seems that the use of appropriate communication; education; feedback, and self-evaluation can be considered a suitable solution to overcome the resistance [ 6 , 50 ]. In general, if the information provided about the change is timely, valid, informative, and sufficient, a more positive evaluation of the change will emerge in the individual [ 51 ]. The tendency to amplify the status quo and the difficulty of change application besides the lack of organizational support can cause resistance to change in nursing [ 35 ]. The lack of participatory management and not being involved in the change process can be considered a factor in the failure of change [ 12 , 27 ]. Employee’s participation in decision-making; Responsibility and ownership of making changes amplify and can be effective in reducing resistance [ 52 ]. Low levels of participation and fear of job loss occur as a result of negative feelings towards change [ 53 ]. It is important to understand cultural change as involving strategic change, which consists of changing an individual’s mind and behavior. How the culture change for each individual is evoked also has an important impact on the result and the consequences for each person [ 54 ]. All noteworthy organizational changes require a few level of corporate culture alter. In spite of the fact that culture alter is essential for making and fortifying organizational change, our position is that making fundamental auxiliary changes may serve as the introductory intercession for changing culture [ 55 ].

Organizational culture is characterized as a set of anticipated behaviors that are for the most part supported inside the group [ 56 ], can play a significant role in RtC. The evidence indicates that to be more successful in the process of change resulting from the implementation of organizational culture, all nurses must be involved in this process from the beginning, otherwise, the employees will feel unappreciated and not involved, and resistance to change will be an unexpected result, and organizational commitment will be reduced [ 36 , 57 , 58 ]. By improving the understanding of the change process, nurses as change agents can meet the challenge of managing change in their clinical environment [ 40 ]. As mentioned; human resources education and amplifying proper communication is among the effective tools to overcome the resistance resulting from the organizational culture [ 6 ]. The nature of the relationship between employees and management, if the pessimism that employees express towards management to change, will mean that they will question the real motivations for implementing the change [ 43 ]. Employees who feel their managers are trustworthy, supportive, inspiring, and can better deal with change; will be more effective in dealing with resistance to change. Therefore, if there is a good relationship between the leadership and the organization’s members, it is expected to see less resistance to change [ 7 ].

Organizational values including organizational culture, negative organizational perception, and conflict with organizational identity also play a fundamental role to cause nurses’ resistance to change. Understanding organizational orientations may hinder the adoption of new evidence-based programs and practices [ 26 ]. Also, changes are frequently in conflict with the organizational identity, which causes an unpleasant impression on individual, and this leads to the distortion of the intended purpose of the change and puts the organizational identity at risk [ 28 ]. Change management starts before any change action is implemented and continues with an understanding of the culture and environmental context in which the change is to be implemented. So, it is important to ensure that change is not just implemented, but that employees and other stakeholders are ready for it [ 24 ].

Structural factors such as organizational characteristics, resources and budget, job characteristics, and environmental changes, along with other organizational factors, are effective factors in creating resistance to change in health care workers. Higgs and Rowland (2010) emphasize factors such as environmental changes, organizational characteristics, resources, and budgets as broad factors affecting the change process [ 36 , 59 ]. Organizational changes are carried out with the aim of changing the way care is provided [ 60 ], one of these changes is related to job characteristics and employees with changes such as moving workplaces, creating new units, merging with existing units, and recruitment of new employee [ 61 ]. Based on this, the key strategies for change management should be focused on the need for sensitivity to organizational culture and characteristics [ 24 ].

Organizational change can be called a stressful factor in the work environment [ 62 ], but although these changes can lead to mental and physical stress among the healthcare team, providing support and positive organizational resources, such as job support and control, may help reduce nurses’ burnout and RtC. Studies also indicate that; when change-related stressors are high, nurses who report high levels of manager’s support; report lower levels of organizational support, and lower levels of cynicism toward workplace change than employees; who report low levels of organizational support [ 63 ]. So, changes in nursing work cause a high workload and increase in administrative stress, which ultimately leads to an increase in pessimism among them regarding the change. Trying to control the job in the organizational structure is necessary to deal with the increase in workload, and reduce pessimism and resistance to change [ 64 ].

Finally, based on the results obtained from selected studies, due to the nature of the nursing profession on the one hand and the occurrence of rapid and large changes in clinical environments and care organizations on other hand, several factors can cause resistance to these changes and affect the care and safety of patients. This resistance is influenced by three important factors, individual, interpersonal, and organizational factors. The effects of these factors, can directly and indirectly, affect the proper care of patients. Therefore, paying attention to these factors to improve them with education, improving communication, efficient and collaborative management, understanding organizational values, and developing organizational structure can reduce resistance to changes in patient care environments.

Changes in the nursing environment are an integral part of nursing practice. The findings of this integrated review confirm the complexity and multifaceted nature of these resistances. A set of individual, interpersonal, and organizational factors in nurses leads to resistance to change and is considered an important challenge in nursing care. Knowing these factors can help reduce resistance and improve the quality of nursing care. So nursing managers and decision makers should pay special attention to this in order to make changes. So that, nurses can provide safe and qualified care for their clients and improve the level of health and satisfaction of patients.


The limitations of this study include: not searching for articles in languages other than English and Persian, so our search strategies may have under-represented studies in other languages, such as Spanish and Portuguese.


Considering that the present study was an integrated review study and the results of other researchers’ studies have been used, the research team would like to express their gratitude to all whose studies were used in this study.

Author contributions

RCH: study design, data collection, and search, analysis and interpretation, drafting of the manuscript; HE: data collection and search. All authors read and approved the final manuscript. NKH: data collection and search.MS: corresponding author, supervision of the review, and critical revision of the final manuscript.

No funds were used to conduct this study.

Data Availability


The author(s) declared no potential conflicts of interest with respect concerning the research, authorship, and/or publication of this article.

Ethical approval was not required to conduct this review.

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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More from our inbox:, orwell in a honda, lessons from covid, diversity in college: more financial aid is needed.

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To the Editor:

Re “ Covid Funds Shrinking, Paid Family Caregivers Face Big Cutbacks ” (news article, March 5):

The story of Kacey Poynter and her son, Sonny, puts a spotlight on the precarious state of family caregiving in America. Indiana’s decision to slash funding for its attendant care program threatens to throw families like Ms. Poynter’s into financial turmoil and disrupt the critical care their loved ones require.

Indiana is just the latest state grappling with this issue. The bigger picture reveals a fragmented system struggling under the weight of insufficient federal investment. The influx of federal pandemic funds offered temporary relief, allowing states like Indiana to expand caregiver support programs.

However, family caregivers were hurting far before the pandemic, with 19 million people reporting high levels of emotional stress. Now, as these funds dry up, states and family caregivers are left scrambling.

To ensure that family caregivers can continue providing vital care for their loved ones, Congress must invest in our nation’s care infrastructure. This includes allocating sufficient funding for home- and community-based services, as well as a national paid family and medical leave program.

Additionally, swift implementation of the National Strategy to Support Family Caregivers , released by the Biden administration in 2022, is essential to recognize the irreplaceable role that caregivers play in our society.

By investing in family caregivers in Indiana and in every state, we’re investing in a stronger future for all families.

Jason Resendez Washington The writer is the president and C.E.O. of the National Alliance for Caregiving.

“ Staffing Shortages at Nursing Homes Persist ” (front page, March 1) reinforces the urgent need for the U.S. to develop a coherent approach to the long-term care needs of our aging population.

The pandemic underscored problems in nursing homes that had long been apparent. It also highlighted a longstanding bias toward institutional care for low-income people. Medicaid, the largest source of funding for long-term services, is required to pay for nursing home care, but not for home- and community-based services.

A National Academy of Social Insurance report , “Social Insurance During the Pandemic: Successes, Shortcomings and Policy Options for the Future,” examines the devastating impact of Covid-19 on our nation’s nursing home residents and staffs. Residents of color were disproportionately harmed; their mortality rates were significantly higher than those of white residents.

Congress needs to consider reforms to increase nursing home staffing and improve pay and working conditions. Congress might also consider expanding the Medicare-funded graduate medical education programs to include nurse training. This would help subsidize the cost of such training and address the nursing shortage in nursing homes.

As your article notes, many experts believe that our current approach to long-term care is “fundamentally broken.” It is time for a national solution.

William J. Arnone Washington The writer is C.E.O. of the National Academy of Social Insurance.

Re “ Watch the Way You’re Driving. Carmakers Are Watching, Too ” (front page, March 12):

I was driving on Interstate 95 in Connecticut recently when a car entering the highway cut me off. I swerved into the left lane, causing my car to fishtail before I regained control. My quick action averted a serious and possibly fatal accident.

That swerve is an example of the kind of noncontextual information that auto insurers are gathering from stealth computer programs in cars like my 2023 Honda Civic. Had I activated “Driver Feedback,” that incident could have led to higher insurance rates for me — instead of for the driver who nearly caused an accident.

In 2024, Big Brother sneaks into the back seat of our cars and watches every move we make. The view from there, however, is not always accurate.

Betty J. Cotter Shannock, R.I.

After reading this article, I feel as if I hit a big pothole going 50 miles an hour. I have questions: What is the car company’s cut for providing information to the insurer? If the insurer charges 21 percent more, as happened to a driver quoted in the article, does the car manufacturer get 10 percent of that?

To generate even more revenue, I suggest that car companies force us to watch commercials (like when you’re filling up at the gas station) on the large screens that are in every car now. Enjoy your drive!

Brant Thomas Cold Spring, N.Y.

Re “ Four Years On, Covid Is Here to Stay ,” by Daniela J. Lamas (Opinion guest essay, March 11):

In her wonderful article, Dr. Lamas beautifully described how she is no longer mortified by Covid but carried its grave lessons forward. As an infectious diseases specialist, I have had similar experiences.

Ignorance is not bliss. To dispel any magical and potentially costly thinking, I want to elaborate on three important lessons.

The first lesson is that science and cooperation prevailed. Let us celebrate and remind ourselves that through mutual respect and a common goal, we were able to tame a deadly virus.

The second lesson is that straightforward and practical infection-control measures such as distancing and quarantining were effective and bought us the time needed to develop a vaccine.

Finally, the third lesson is that the vaccine worked.

Like it or not, Covid is here to stay. We will all need to boldly accept this fact. We need not be fearful, though, because we now understand it and have hopefully learned at least three critical lessons that will prevent Covid from resurging and causing another deadly pandemic.

Prescott Lee Boston

nursing research study articles

Can You Create a Diverse College Class Without Affirmative Action?

The Supreme Court effectively ended race-based admissions preferences. But will selective schools still be able to achieve diverse student bodies? Here’s how they might try.

Re “ Can You Create a Diverse College Class Without Affirmative Action? ” (The Upshot, nytimes.com, March 9):

The analysis in your piece shows that highly selective colleges might achieve racial diversity using race-blind approaches if they put extensive weight on socioeconomic factors.

Our own analysis produced similar findings. But we also show that such a change would require a substantial increase in financial aid so that low-income students could afford to enroll. For all but perhaps a dozen or two institutions that have very large endowments, that is likely more than they can muster.

In fact, financial aid already falls $10 billion short of what low-income students at selective colleges need. The logic is simple: Swapping out 35 percent of high-income students for lower-income students, as in one of your simulations, would be very expensive. The newly selected students would need tens of thousands of dollars in financial aid per year.

Increasing the enrollment of lower-income and Black, Latino and Native American students at selective colleges is an important goal that institutions should prioritize. But the cost would be substantial. Insufficient financial aid is a problem across higher education, one that makes using income-based admissions preferences like those described in the Upshot analysis an uphill climb.

Phillip Levine Sarah Reber Dr. Levine is a professor of economics at Wellesley College and a nonresident senior fellow at the Brookings Institution. Dr. Reber is a senior fellow in economic studies at Brookings.


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