qualitative research methods using secondary data

Secondary Qualitative Research Methodology Using Online Data within the Context of Social Sciences

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

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Home » Secondary Data – Types, Methods and Examples

Secondary Data – Types, Methods and Examples

Table of Contents

Secondary Data

Definition:

Secondary data refers to information that has been collected, processed, and published by someone else, rather than the researcher gathering the data firsthand. This can include data from sources such as government publications, academic journals, market research reports, and other existing datasets.

Secondary Data Types

Types of secondary data are as follows:

• Published data: Published data refers to data that has been published in books, magazines, newspapers, and other print media. Examples include statistical reports, market research reports, and scholarly articles.
• Government data: Government data refers to data collected by government agencies and departments. This can include data on demographics, economic trends, crime rates, and health statistics.
• Commercial data: Commercial data is data collected by businesses for their own purposes. This can include sales data, customer feedback, and market research data.
• Academic data: Academic data refers to data collected by researchers for academic purposes. This can include data from experiments, surveys, and observational studies.
• Online data: Online data refers to data that is available on the internet. This can include social media posts, website analytics, and online customer reviews.
• Organizational data: Organizational data is data collected by businesses or organizations for their own purposes. This can include data on employee performance, financial records, and customer satisfaction.
• Historical data : Historical data refers to data that was collected in the past and is still available for research purposes. This can include census data, historical documents, and archival records.
• International data: International data refers to data collected from other countries for research purposes. This can include data on international trade, health statistics, and demographic trends.
• Public data : Public data refers to data that is available to the general public. This can include data from government agencies, non-profit organizations, and other sources.
• Private data: Private data refers to data that is not available to the general public. This can include confidential business data, personal medical records, and financial data.
• Big data: Big data refers to large, complex datasets that are difficult to manage and analyze using traditional data processing methods. This can include social media data, sensor data, and other types of data generated by digital devices.

Secondary Data Collection Methods

Secondary Data Collection Methods are as follows:

• Published sources: Researchers can gather secondary data from published sources such as books, journals, reports, and newspapers. These sources often provide comprehensive information on a variety of topics.
• Online sources: With the growth of the internet, researchers can now access a vast amount of secondary data online. This includes websites, databases, and online archives.
• Government sources : Government agencies often collect and publish a wide range of secondary data on topics such as demographics, crime rates, and health statistics. Researchers can obtain this data through government websites, publications, or data portals.
• Commercial sources: Businesses often collect and analyze data for marketing research or customer profiling. Researchers can obtain this data through commercial data providers or by purchasing market research reports.
• Academic sources: Researchers can also obtain secondary data from academic sources such as published research studies, academic journals, and dissertations.
• Personal contacts: Researchers can also obtain secondary data from personal contacts, such as experts in a particular field or individuals with specialized knowledge.

Secondary Data Formats

Secondary data can come in various formats depending on the source from which it is obtained. Here are some common formats of secondary data:

• Numeric Data: Numeric data is often in the form of statistics and numerical figures that have been compiled and reported by organizations such as government agencies, research institutions, and commercial enterprises. This can include data such as population figures, GDP, sales figures, and market share.
• Textual Data: Textual data is often in the form of written documents, such as reports, articles, and books. This can include qualitative data such as descriptions, opinions, and narratives.
• Audiovisual Data : Audiovisual data is often in the form of recordings, videos, and photographs. This can include data such as interviews, focus group discussions, and other types of qualitative data.
• Geospatial Data: Geospatial data is often in the form of maps, satellite images, and geographic information systems (GIS) data. This can include data such as demographic information, land use patterns, and transportation networks.
• Transactional Data : Transactional data is often in the form of digital records of financial and business transactions. This can include data such as purchase histories, customer behavior, and financial transactions.
• Social Media Data: Social media data is often in the form of user-generated content from social media platforms such as Facebook, Twitter, and Instagram. This can include data such as user demographics, content trends, and sentiment analysis.

Secondary Data Analysis Methods

Secondary data analysis involves the use of pre-existing data for research purposes. Here are some common methods of secondary data analysis:

• Descriptive Analysis: This method involves describing the characteristics of a dataset, such as the mean, standard deviation, and range of the data. Descriptive analysis can be used to summarize data and provide an overview of trends.
• Inferential Analysis: This method involves making inferences and drawing conclusions about a population based on a sample of data. Inferential analysis can be used to test hypotheses and determine the statistical significance of relationships between variables.
• Content Analysis: This method involves analyzing textual or visual data to identify patterns and themes. Content analysis can be used to study the content of documents, media coverage, and social media posts.
• Time-Series Analysis : This method involves analyzing data over time to identify trends and patterns. Time-series analysis can be used to study economic trends, climate change, and other phenomena that change over time.
• Spatial Analysis : This method involves analyzing data in relation to geographic location. Spatial analysis can be used to study patterns of disease spread, land use patterns, and the effects of environmental factors on health outcomes.
• Meta-Analysis: This method involves combining data from multiple studies to draw conclusions about a particular phenomenon. Meta-analysis can be used to synthesize the results of previous research and provide a more comprehensive understanding of a particular topic.

Secondary Data Gathering Guide

Here are some steps to follow when gathering secondary data:

• Define your research question: Start by defining your research question and identifying the specific information you need to answer it. This will help you identify the type of secondary data you need and where to find it.
• Identify relevant sources: Identify potential sources of secondary data, including published sources, online databases, government sources, and commercial data providers. Consider the reliability and validity of each source.
• Evaluate the quality of the data: Evaluate the quality and reliability of the data you plan to use. Consider the data collection methods, sample size, and potential biases. Make sure the data is relevant to your research question and is suitable for the type of analysis you plan to conduct.
• Collect the data: Collect the relevant data from the identified sources. Use a consistent method to record and organize the data to make analysis easier.
• Validate the data: Validate the data to ensure that it is accurate and reliable. Check for inconsistencies, missing data, and errors. Address any issues before analyzing the data.
• Analyze the data: Analyze the data using appropriate statistical and analytical methods. Use descriptive and inferential statistics to summarize and draw conclusions from the data.
• Interpret the results: Interpret the results of your analysis and draw conclusions based on the data. Make sure your conclusions are supported by the data and are relevant to your research question.
• Communicate the findings : Communicate your findings clearly and concisely. Use appropriate visual aids such as graphs and charts to help explain your results.

Examples of Secondary Data

Here are some examples of secondary data from different fields:

• Healthcare : Hospital records, medical journals, clinical trial data, and disease registries are examples of secondary data sources in healthcare. These sources can provide researchers with information on patient demographics, disease prevalence, and treatment outcomes.
• Marketing : Market research reports, customer surveys, and sales data are examples of secondary data sources in marketing. These sources can provide marketers with information on consumer preferences, market trends, and competitor activity.
• Education : Student test scores, graduation rates, and enrollment statistics are examples of secondary data sources in education. These sources can provide researchers with information on student achievement, teacher effectiveness, and educational disparities.
• Finance : Stock market data, financial statements, and credit reports are examples of secondary data sources in finance. These sources can provide investors with information on market trends, company performance, and creditworthiness.
• Social Science : Government statistics, census data, and survey data are examples of secondary data sources in social science. These sources can provide researchers with information on population demographics, social trends, and political attitudes.
• Environmental Science : Climate data, remote sensing data, and ecological monitoring data are examples of secondary data sources in environmental science. These sources can provide researchers with information on weather patterns, land use, and biodiversity.

Purpose of Secondary Data

The purpose of secondary data is to provide researchers with information that has already been collected by others for other purposes. Secondary data can be used to support research questions, test hypotheses, and answer research objectives. Some of the key purposes of secondary data are:

• To gain a better understanding of the research topic : Secondary data can be used to provide context and background information on a research topic. This can help researchers understand the historical and social context of their research and gain insights into relevant variables and relationships.
• To save time and resources: Collecting new primary data can be time-consuming and expensive. Using existing secondary data sources can save researchers time and resources by providing access to pre-existing data that has already been collected and organized.
• To provide comparative data : Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• To support triangulation: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• To supplement primary data : Secondary data can be used to supplement primary data by providing additional information or insights that were not captured by the primary research. This can help researchers gain a more complete understanding of the research topic and draw more robust conclusions.

When to use Secondary Data

Secondary data can be useful in a variety of research contexts, and there are several situations in which it may be appropriate to use secondary data. Some common situations in which secondary data may be used include:

• When primary data collection is not feasible : Collecting primary data can be time-consuming and expensive, and in some cases, it may not be feasible to collect primary data. In these situations, secondary data can provide valuable insights and information.
• When exploring a new research area : Secondary data can be a useful starting point for researchers who are exploring a new research area. Secondary data can provide context and background information on a research topic, and can help researchers identify key variables and relationships to explore further.
• When comparing and contrasting research findings: Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• When triangulating research findings: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• When validating research findings : Secondary data can be used to validate primary research findings by providing additional sources of data that support or refute the primary findings.

Characteristics of Secondary Data

Secondary data have several characteristics that distinguish them from primary data. Here are some of the key characteristics of secondary data:

• Non-reactive: Secondary data are non-reactive, meaning that they are not collected for the specific purpose of the research study. This means that the researcher has no control over the data collection process, and cannot influence how the data were collected.
• Time-saving: Secondary data are pre-existing, meaning that they have already been collected and organized by someone else. This can save the researcher time and resources, as they do not need to collect the data themselves.
• Wide-ranging : Secondary data sources can provide a wide range of information on a variety of topics. This can be useful for researchers who are exploring a new research area or seeking to compare and contrast research findings.
• Less expensive: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Potential for bias : Secondary data may be subject to biases that were present in the original data collection process. For example, data may have been collected using a biased sampling method or the data may be incomplete or inaccurate.
• Lack of control: The researcher has no control over the data collection process and cannot ensure that the data were collected using appropriate methods or measures.
• Requires careful evaluation : Secondary data sources must be evaluated carefully to ensure that they are appropriate for the research question and analysis. This includes assessing the quality, reliability, and validity of the data sources.

Advantages of Secondary Data

There are several advantages to using secondary data in research, including:

• Time-saving : Collecting primary data can be time-consuming and expensive. Secondary data can be accessed quickly and easily, which can save researchers time and resources.
• Cost-effective: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Large sample size : Secondary data sources often have larger sample sizes than primary data sources, which can increase the statistical power of the research.
• Access to historical data : Secondary data sources can provide access to historical data, which can be useful for researchers who are studying trends over time.
• No ethical concerns: Secondary data are already in existence, so there are no ethical concerns related to collecting data from human subjects.
• May be more objective : Secondary data may be more objective than primary data, as the data were not collected for the specific purpose of the research study.

Limitations of Secondary Data

While there are many advantages to using secondary data in research, there are also some limitations that should be considered. Some of the main limitations of secondary data include:

• Lack of control over data quality : Researchers do not have control over the data collection process, which means they cannot ensure the accuracy or completeness of the data.
• Limited availability: Secondary data may not be available for the specific research question or study design.
• Lack of information on sampling and data collection methods: Researchers may not have access to information on the sampling and data collection methods used to gather the secondary data. This can make it difficult to evaluate the quality of the data.
• Data may not be up-to-date: Secondary data may not be up-to-date or relevant to the current research question.
• Data may be incomplete or inaccurate : Secondary data may be incomplete or inaccurate due to missing or incorrect data points, data entry errors, or other factors.
• Biases in data collection: The data may have been collected using biased sampling or data collection methods, which can limit the validity of the data.
• Lack of control over variables: Researchers have limited control over the variables that were measured in the original data collection process, which can limit the ability to draw conclusions about causality.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Secondary research: definition, methods, & examples.

19 min read This ultimate guide to secondary research helps you understand changes in market trends, customers buying patterns and your competition using existing data sources.

In situations where you’re not involved in the data gathering process ( primary research ), you have to rely on existing information and data to arrive at specific research conclusions or outcomes. This approach is known as secondary research.

In this article, we’re going to explain what secondary research is, how it works, and share some examples of it in practice.

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What is secondary research?

Secondary research, also known as desk research, is a research method that involves compiling existing data sourced from a variety of channels . This includes internal sources (e.g.in-house research) or, more commonly, external sources (such as government statistics, organizational bodies, and the internet).

Secondary research comes in several formats, such as published datasets, reports, and survey responses , and can also be sourced from websites, libraries, and museums.

The information is usually free — or available at a limited access cost — and gathered using surveys , telephone interviews, observation, face-to-face interviews, and more.

When using secondary research, researchers collect, verify, analyze and incorporate it to help them confirm research goals for the research period.

As well as the above, it can be used to review previous research into an area of interest. Researchers can look for patterns across data spanning several years and identify trends — or use it to verify early hypothesis statements and establish whether it’s worth continuing research into a prospective area.

How to conduct secondary research

There are five key steps to conducting secondary research effectively and efficiently:

1.    Identify and define the research topic

First, understand what you will be researching and define the topic by thinking about the research questions you want to be answered.

Ask yourself: What is the point of conducting this research? Then, ask: What do we want to achieve?

This may indicate an exploratory reason (why something happened) or confirm a hypothesis. The answers may indicate ideas that need primary or secondary research (or a combination) to investigate them.

2.    Find research and existing data sources

If secondary research is needed, think about where you might find the information. This helps you narrow down your secondary sources to those that help you answer your questions. What keywords do you need to use?

Which organizations are closely working on this topic already? Are there any competitors that you need to be aware of?

Create a list of the data sources, information, and people that could help you with your work.

3.    Begin searching and collecting the existing data

Now that you have the list of data sources, start accessing the data and collect the information into an organized system. This may mean you start setting up research journal accounts or making telephone calls to book meetings with third-party research teams to verify the details around data results.

As you search and access information, remember to check the data’s date, the credibility of the source, the relevance of the material to your research topic, and the methodology used by the third-party researchers. Start small and as you gain results, investigate further in the areas that help your research’s aims.

4.    Combine the data and compare the results

When you have your data in one place, you need to understand, filter, order, and combine it intelligently. Data may come in different formats where some data could be unusable, while other information may need to be deleted.

After this, you can start to look at different data sets to see what they tell you. You may find that you need to compare the same datasets over different periods for changes over time or compare different datasets to notice overlaps or trends. Ask yourself: What does this data mean to my research? Does it help or hinder my research?

5.    Analyze your data and explore further

In this last stage of the process, look at the information you have and ask yourself if this answers your original questions for your research. Are there any gaps? Do you understand the information you’ve found? If you feel there is more to cover, repeat the steps and delve deeper into the topic so that you can get all the information you need.

If secondary research can’t provide these answers, consider supplementing your results with data gained from primary research. As you explore further, add to your knowledge and update your findings. This will help you present clear, credible information.

Primary vs secondary research

Unlike secondary research, primary research involves creating data first-hand by directly working with interviewees, target users, or a target market. Primary research focuses on the method for carrying out research, asking questions, and collecting data using approaches such as:

• Interviews (panel, face-to-face or over the phone)
• Questionnaires or surveys
• Focus groups

Using these methods, researchers can get in-depth, targeted responses to questions, making results more accurate and specific to their research goals. However, it does take time to do and administer.

Unlike primary research, secondary research uses existing data, which also includes published results from primary research. Researchers summarize the existing research and use the results to support their research goals.

Both primary and secondary research have their places. Primary research can support the findings found through secondary research (and fill knowledge gaps), while secondary research can be a starting point for further primary research. Because of this, these research methods are often combined for optimal research results that are accurate at both the micro and macro level.

Sources of Secondary Research

There are two types of secondary research sources: internal and external. Internal data refers to in-house data that can be gathered from the researcher’s organization. External data refers to data published outside of and not owned by the researcher’s organization.

Internal data

Internal data is a good first port of call for insights and knowledge, as you may already have relevant information stored in your systems. Because you own this information — and it won’t be available to other researchers — it can give you a competitive edge . Examples of internal data include:

• Database information on sales history and business goal conversions
• Information from website applications and mobile site data
• Customer-generated data on product and service efficiency and use
• Previous research results or supplemental research areas
• Previous campaign results

External data

External data is useful when you: 1) need information on a new topic, 2) want to fill in gaps in your knowledge, or 3) want data that breaks down a population or market for trend and pattern analysis. Examples of external data include:

• Government, non-government agencies, and trade body statistics
• Company reports and research
• Competitor research
• Public library collections
• Textbooks and research journals
• Media stories in newspapers
• Online journals and research sites

Three examples of secondary research methods in action

How and why might you conduct secondary research? Let’s look at a few examples:

1.    Collecting factual information from the internet on a specific topic or market

There are plenty of sites that hold data for people to view and use in their research. For example, Google Scholar, ResearchGate, or Wiley Online Library all provide previous research on a particular topic. Researchers can create free accounts and use the search facilities to look into a topic by keyword, before following the instructions to download or export results for further analysis.

This can be useful for exploring a new market that your organization wants to consider entering. For instance, by viewing the U.S Census Bureau demographic data for that area, you can see what the demographics of your target audience are , and create compelling marketing campaigns accordingly.

2.    Finding out the views of your target audience on a particular topic

If you’re interested in seeing the historical views on a particular topic, for example, attitudes to women’s rights in the US, you can turn to secondary sources.

Textbooks, news articles, reviews, and journal entries can all provide qualitative reports and interviews covering how people discussed women’s rights. There may be multimedia elements like video or documented posters of propaganda showing biased language usage.

By gathering this information, synthesizing it, and evaluating the language, who created it and when it was shared, you can create a timeline of how a topic was discussed over time.

3.    When you want to know the latest thinking on a topic

Educational institutions, such as schools and colleges, create a lot of research-based reports on younger audiences or their academic specialisms. Dissertations from students also can be submitted to research journals, making these places useful places to see the latest insights from a new generation of academics.

Information can be requested — and sometimes academic institutions may want to collaborate and conduct research on your behalf. This can provide key primary data in areas that you want to research, as well as secondary data sources for your research.

Advantages of secondary research

There are several benefits of using secondary research, which we’ve outlined below:

• Easily and readily available data – There is an abundance of readily accessible data sources that have been pre-collected for use, in person at local libraries and online using the internet. This data is usually sorted by filters or can be exported into spreadsheet format, meaning that little technical expertise is needed to access and use the data.
• Faster research speeds – Since the data is already published and in the public arena, you don’t need to collect this information through primary research. This can make the research easier to do and faster, as you can get started with the data quickly.
• Low financial and time costs – Most secondary data sources can be accessed for free or at a small cost to the researcher, so the overall research costs are kept low. In addition, by saving on preliminary research, the time costs for the researcher are kept down as well.
• Secondary data can drive additional research actions – The insights gained can support future research activities (like conducting a follow-up survey or specifying future detailed research topics) or help add value to these activities.
• Secondary data can be useful pre-research insights – Secondary source data can provide pre-research insights and information on effects that can help resolve whether research should be conducted. It can also help highlight knowledge gaps, so subsequent research can consider this.
• Ability to scale up results – Secondary sources can include large datasets (like Census data results across several states) so research results can be scaled up quickly using large secondary data sources.

Disadvantages of secondary research

The disadvantages of secondary research are worth considering in advance of conducting research :

• Secondary research data can be out of date – Secondary sources can be updated regularly, but if you’re exploring the data between two updates, the data can be out of date. Researchers will need to consider whether the data available provides the right research coverage dates, so that insights are accurate and timely, or if the data needs to be updated. Also, fast-moving markets may find secondary data expires very quickly.
• Secondary research needs to be verified and interpreted – Where there’s a lot of data from one source, a researcher needs to review and analyze it. The data may need to be verified against other data sets or your hypotheses for accuracy and to ensure you’re using the right data for your research.
• The researcher has had no control over the secondary research – As the researcher has not been involved in the secondary research, invalid data can affect the results. It’s therefore vital that the methodology and controls are closely reviewed so that the data is collected in a systematic and error-free way.
• Secondary research data is not exclusive – As data sets are commonly available, there is no exclusivity and many researchers can use the same data. This can be problematic where researchers want to have exclusive rights over the research results and risk duplication of research in the future.

When do we conduct secondary research?

Now that you know the basics of secondary research, when do researchers normally conduct secondary research?

It’s often used at the beginning of research, when the researcher is trying to understand the current landscape . In addition, if the research area is new to the researcher, it can form crucial background context to help them understand what information exists already. This can plug knowledge gaps, supplement the researcher’s own learning or add to the research.

Secondary research can also be used in conjunction with primary research. Secondary research can become the formative research that helps pinpoint where further primary research is needed to find out specific information. It can also support or verify the findings from primary research.

You can use secondary research where high levels of control aren’t needed by the researcher, but a lot of knowledge on a topic is required from different angles.

Secondary research should not be used in place of primary research as both are very different and are used for various circumstances.

Questions to ask before conducting secondary research

Before you start your secondary research, ask yourself these questions:

• Is there similar internal data that we have created for a similar area in the past?

If your organization has past research, it’s best to review this work before starting a new project. The older work may provide you with the answers, and give you a starting dataset and context of how your organization approached the research before. However, be mindful that the work is probably out of date and view it with that note in mind. Read through and look for where this helps your research goals or where more work is needed.

• What am I trying to achieve with this research?

When you have clear goals, and understand what you need to achieve, you can look for the perfect type of secondary or primary research to support the aims. Different secondary research data will provide you with different information – for example, looking at news stories to tell you a breakdown of your market’s buying patterns won’t be as useful as internal or external data e-commerce and sales data sources.

• How credible will my research be?

If you are looking for credibility, you want to consider how accurate the research results will need to be, and if you can sacrifice credibility for speed by using secondary sources to get you started. Bear in mind which sources you choose — low-credibility data sites, like political party websites that are highly biased to favor their own party, would skew your results.

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Ianna Journal of Interdisciplinary Studies (Aug 2024)

Can the Digital Software Method Outperform the Manual Method in Qualitative Data Analysis? Findings from a Quasi-experimental Research

• Ugochukwu Simeon Asogwa,
• Hannah Ifedapo Maiyekogbon,
• Margaret Offoboche Agada-Mba,
• Oluwaseyi John Jemisenia

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Background: In the dynamic field of qualitative research, a contentious issue persists: Is digital software a more effective tool for research analysis than the manual method? To shed light on this debate, we undertook quasi-experimental research, focusing on our study's unique contribution to exploring the capabilities of both methods in analysing health datasets. Objective: Our study aims to compare the effectiveness of qualitative analysis between researchers who are proficient in digital software and those skilled in the manual method. We seek to understand which method is more effective in data analysis. Methodology: We employed a quasi-experimental design and a purposive sampling approach to select our study participants. These participants (n=150) were then divided into two groups: those proficient in digital software and those skilled in the manual method. We then conducted an intervention in which participants analysed a qualitative dataset using their preferred method. The data collected was then analysed using quantitative measures, such as percentages, central tendency measures, and independent samples t-tests. Results: The t-test result showed that statistically significant differences exist between the two groups across all indicators (all Ps<.0001). Specific observation of the mean scores revealed that for perceived efficiency (M=3.50 [SD=0.55]), productivity (M=3.40 [SD=0.60]), collaboration (M=3.55 [SD=0.50]), identification of complex themes (M=3.60 [0.45]), and visualisation techniques(M=3.60 [SD=0.45]), participants who used digital software scored higher than those who used manual method of data analysis. However, for perceived depth of analysis (M=3.50 [SD=0.55]), coding flexibility(M=3.45 [SD=0.50]), reflective quality(M=3.60 [SD=0.50]) and integration of contextual knowledge(M=3.55 [SD=0.45]), participants in the manual method group scored higher compared to those in the digital software group Contribution: This study adds to burgeoning and existing knowledge on the need for a complementary approach to adopting and using digital tools and manual methods in conducting qualitative data analysis. Although using both methods can offer many benefits, it is crucial to use the advantages of one method to address the drawbacks of the other where possible. While these benefits should be observed when combining both methods, the challenges of both methods must be acknowledged. Conclusion: This study emphasises the complementary advantages of digital and manual qualitative data analysis methods. Recommendation: A well-rounded strategy that uses the benefits of both approaches is advised to provide thorough and complex qualitative research results.

• Qualitative data analysis
• Digital software
• Manual methods
• Quasi-experimental research

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Peer-reviewed

Research Article

Barriers and facilitators for implementing the WHO Safe Childbirth Checklist (SCC) in Mozambique: A qualitative study using the Consolidated Framework for Implementation Research (CFIR)

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Current address: Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut, United States of America

Affiliation Department of Health Policy, Yale School of Public Health, New Haven, Connecticut, United States of America

Roles Data curation, Investigation, Methodology, Project administration, Resources, Validation

Affiliations Comité para Saúde de Moçambique, Maputo City, Mozambique, Mozambique Ministry of Health, Maputo City, Mozambique

Roles Validation, Writing – review & editing

Affiliation Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut, United States of America

Roles Formal analysis

Roles Resources, Supervision

Affiliation Mozambique Ministry of Health, Maputo City, Mozambique

Roles Conceptualization, Supervision

Affiliation Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, United States of America

Roles Data curation, Methodology

Affiliation Comité para Saúde de Moçambique, Maputo City, Mozambique

Affiliation Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America

Roles Conceptualization, Methodology, Resources, Supervision, Validation, Writing – review & editing

Affiliation Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America

Roles Conceptualization, Data curation, Methodology, Resources, Validation, Writing – review & editing

Affiliation Department of Health Systems and Global Health, Southern Medical University, Guangzhou, Guangdong, China

• Anqi He, 
• Elsa Luís Kanduma, 
• Rafael Pérez-Escamilla, 
• Devina Buckshee, 
• Eusébio Chaquisse, 
• Rosa Marlene Cuco, 
• Mayur Mahesh Desai, 
• Danícia Munguambe, 
• Sakina Erika Reames, 

• Published: September 5, 2024
• https://doi.org/10.1371/journal.pgph.0003174
• Reader Comments

High maternal and neonatal mortality rates persist in Mozambique, with stillbirths remaining understudied. Most maternal and neonatal deaths in the country are due to preventable and treatable childbirth-related complications that often occur in low-resource settings. The World Health Organization introduced the Safe Childbirth Checklist (SCC) in 2015 to reduce adverse birth outcomes. The SCC, a structured list of evidence-based practices, targets the main causes of maternal and neonatal deaths and stillbirths in healthcare facilities. The SCC has been tested in over 35 countries, demonstrating its ability to improve the quality of care. However, it has not been adopted in Mozambique. This study aimed to identify potential facilitators and barriers to SCC implementation from the perspective of birth attendants, clinical administrators, and decision-makers to inform future SCC implementation in Mozambique. We conducted a qualitative study involving focus group discussions with birth attendants (n = 24) and individual interviews with clinical administrators (n = 6) and decision-makers (n = 8). The Consolidated Framework for Implementation Research guided the questions used in the interviews and focus group discussions, as well as the subsequent data analysis. A deductive thematic analysis of Portuguese-to-English translated transcripts was performed. In Mozambique, most barriers to potential SCC implementation stem from the challenges within a weak health system, including underfunded maternal care, lack of infrastructure and human resources, and low provider motivation. The simplicity of the SCC and the commitment of healthcare providers to better childbirth practices, combined with their willingness to adopt the SCC, were identified as major facilitators. To improve the feasibility of SCC implementation and increase compatibility with current childbirth routines for birth attendants, the SCC should be tailored to context-specific needs. Future research should prioritize conducting pre-implementation assessments to align the SCC more effectively with local contexts and facilitate sustainable enhancements in childbirth practices.

Citation: He A, Kanduma EL, Pérez-Escamilla R, Buckshee D, Chaquisse E, Cuco RM, et al. (2024) Barriers and facilitators for implementing the WHO Safe Childbirth Checklist (SCC) in Mozambique: A qualitative study using the Consolidated Framework for Implementation Research (CFIR). PLOS Glob Public Health 4(9): e0003174. https://doi.org/10.1371/journal.pgph.0003174

Editor: Julia Robinson, PLOS: Public Library of Science, UNITED STATES OF AMERICA

Received: January 2, 2024; Accepted: August 8, 2024; Published: September 5, 2024

Copyright: © 2024 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Our article includes selected excerpts from the qualitative data we collected and synthesized. When we sought ethical approval from the National Committee for Bioethics in Health (CNBS) in Mozambique and conducted the consent process with participants, we did not specify that the full transcripts would be made publicly available. Many of the topics discussed in interviews were of a sensitive nature, and participants may not have felt comfortable sharing their perspectives if we had asked to make the conversations public. Therefore, we feel that releasing full transcripts would not adhere to our ethics and consent practices, and would like to share further information only upon request. For those interested in accessing the interview transcripts, access requests can be directed to the National Committee for Bioethics in Health (CNBS) at [email protected] or to the study PI at [email protected] .

Funding: This work was supported by grants from the 2022 Wilbur G. Downs Fellowship at Yale University (AH, US$4,000), the 2022 Yale School of Medicine Fellowship for Medical Student Research (AH, US$2,000), and the 2022 Lindsay Fellowship for Research in Africa from the Yale MacMillan Center’s Council on African Studies (AH, US$1,000). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Wilbur G. Downs Fellowship: https://bit.ly/3aAvJCk Yale School of Medicine Fellowship: Internal application that doesn’t have a URL. Lindsay Fellowship for Research in Africa: https://bit.ly/3roHFh4 .

Competing interests: The authors have declared that no competing interests exist.

Introduction

The global efforts towards achieving the World Health Organization (WHO) Sustainable Development Goal 3 (Ensure healthy lives and promote well-being for all at all ages) have significantly reduced pregnancy-related deaths, especially in sub-Saharan Africa (SSA) [ 1 ], one of the regions most affected by maternal and neonatal mortality in the world [ 2 ]. Guided by the Mozambican Strategic Plan for Health Sector 2014–19 and the Government’s Five-Year Plan 2020–24 [ 3 , 4 ], the Mozambican government has substantially improved maternal and child health (MCH) outcomes by expanding care services and enhancing their quality. Between 2015 and 2021, maternal mortality in Mozambique decreased by 75.8% [ 5 ], neonatal mortality by 8% [ 6 ], and stillbirth rates declined by 7.4% [ 7 ]. While Mozambique shares a similar neonatal mortality ratio of 27 per 1,000 live births [ 8 ] and a stillbirth rate of 17 per 1,000 total births with overall SSA [ 9 ], it has a significantly lower maternal mortality ratio (MMR) of 127 deaths per 100,000 live births compared to the overall MMR of 536 deaths per 100,000 live births in SSA [ 10 ].

Despite these improvements, maternal and neonatal mortality ratios and stillbirth rates remain unacceptably high in Mozambique with pregnancy and childbirth complications as the leading causes: 86% of maternal deaths result from direct obstetric complications [ 11 ], and 75% of newborn deaths are caused by prematurity, childbirth-related complications, and neonatal infections [ 12 ]. Most of these deaths are preventable and treatable but continue to occur at high rates in low-resource settings [ 13 ].

To address maternal and perinatal morbidity and mortality, the WHO developed the Safe Childbirth Checklist (SCC) in 2015 (see S1 Text ) [ 13 ]. The SCC sets forth a structured list of evidence-based delivery practices which target the major causes of maternal deaths, neonatal deaths, and stillbirths in healthcare facilities, especially in lower- and middle-income countries (LMICs). The SCC streamlines the routine flow of childbirth delivery events into four pause points at which birth attendants ensure that they have completed essential birth practices: (a) on admission, (b) just before pushing (or just before a Caesarean-section), (c) soon after birth, and (d) just before discharge. The SCC prompts birth attendants to implement essential practices which have been shown to improve the quality of care delivered to mothers. A birth attendant’s omission of even one of the SCC items can render the mother and their newborn vulnerable to serious and potentially lethal complications.

The SCC has been implemented and evaluated in over 35 countries, demonstrating varied levels of effectiveness in reducing childbirth complications and improving maternal and newborn health outcomes [ 14 ]. Previous studies conducted in India, Ethiopia, Tanzania, Sri Lanka, Bangladesh, Kenya, Uganda, and Namibia have demonstrated that the implementation of SCC contributed to the overall improvement of the quality of care for mothers and newborns [ 15 – 21 ]. Key findings indicate that SCC adoption leads to increased birth attendant adherence to essential birth practices, improved inventory management for essential supplies, facilitated clinical decision-making, enhanced communication and teamwork among providers, and better management of complications. Moreover, research conducted across various settings has highlighted the significant impact of the SCC in reducing perinatal mortality and stillbirths. In Namibia, Kenya, Uganda, and Rajasthan, India, the implementation of the SCC was associated with decreased perinatal mortality, including facility-based stillbirths, very early neonatal deaths, and neonatal mortality among low-birthweight and preterm infants [ 18 , 21 , 22 ]. Moreover, a post-hoc analysis from the BetterBirth trial in Uttar Pradesh, India, revealed significantly lower odds of perinatal and early neonatal mortality with each additional SCC practice performed [ 15 ].

At least 11 countries in Africa have adopted and adapted the SCC: Rwanda, Ethiopia, Burkina Faso, Guinea, Côte d’Ivoire, Mali, Nigeria, Tanzania, Uganda, Kenya, and Namibia [ 14 , 16 – 18 , 23 – 25 ]. The experiences in these countries have provided fresh and valuable insights into local adaptations, facilitators, and barriers to successful implementation of the SCC [ 26 , 27 ]. The primary facilitators of SCC implementation were characteristics inherent to the checklist itself, including its ease of completion and comprehension, and its effectiveness as a job aid for essential practices [ 14 ]. Additional enabling factors identified included leadership commitment, provider motivation, and comprehensive training and supervision regarding SCC usage [ 14 , 23 ]. Barriers to SCC implementation frequently related to a shortage of clinical staff and essential birth and checklist supplies, a lack of professional training on the SCC, perceptions of increased workload due to the SCC usage, and challenges that often coincided with delivering quality maternal care [ 14 ]. Therefore, as research across multiple regions has underscored, adapting the SCC to the local context is crucial to align it with local guidelines and for its adoption by healthcare professionals. For example, in Burkina Faso and Côte d’Ivoire, health providers suggested integrating the SCC with existing tools like the partograph and displaying it in maternity wards as a reminder of critical birth practices [ 23 ]. In Kenya and Uganda, local modifications aimed at enhancing preterm birth outcomes included integrating a triage pause for initial assessments, focusing on assessing gestational age and managing preterm labor, and adjusting the SCC to better align with national care standards [ 25 ].

Despite its strong potential to improve maternal and newborn health outcomes, the SCC has not been adopted in Mozambique, one of the poorest countries in the world with major infrastructural constraints in its healthcare system, which could potentially benefit from the SCC implementation. Advancing improvements in lowering maternal and neonatal mortality, along with enhancing the overall health of the population, are key strategic aims outlined in the Mozambique Government’s 2020–2024 Five-Year Plan [ 4 ]. These objectives are also central to the UNICEF-Mozambique 2022–2026 Strategic Plan and key to the UNDP-Mozambique collaboration goals [ 28 , 29 ]. Although various national guidelines specific to certain procedures and complications during childbirth exist, they are not systematically integrated as in the SCC. Moreover, little is known about current childbirth practices in Mozambique and the feasibility and acceptability of adopting the SCC in local healthcare facilities. This formative study aims to identify facilitators and barriers to potential implementation of the SCC in Mozambique, provide insights into current childbirth practices and infrastructure in the country, and guide the Mozambique Ministry of Health (MoH)’s decisions on SCC adoption and adaptation to improve MCH outcomes nationwide.

Study setting

In Mozambique, the public health system is organized and administered at the national, provincial, and district levels. This structure includes four levels of health facilities, each with distinct roles and capacities. Maternity care is similarly organized within this structure [ 30 ].

Primary-level health facilities, designated as health centers, serve as the primary point of contact for the population. They provide primary health care and are classified as urban or rural based on their location, with some only having the minimal capacity to perform vaginal childbirth deliveries and others not being able to do so [ 31 ]. Secondary-level hospitals, divided into district, rural, and general hospitals, provide referral care, emergency services, and surgeries. They provide more comprehensive maternity services such as assisted deliveries and basic obstetric surgeries, but their capacity to perform C-sections varies by hospital. Tertiary and quaternary-level hospitals, which include provincial, central, and referral hospitals, provide specialized care and serve as referral centers with the capacity to offer advanced and comprehensive obstetric and neonatal care, including emergency C-sections for complicated pregnancies and births.

The study was conducted in Maputo city and Manhiça district in Maputo province, Mozambique. Maputo city is the capital and the largest city of Mozambique with a population of 1.09 million in 2017 [ 32 ]. It is located at the southern end of the country, close to Mozambique’s border with Eswatini and South Africa. The city is divided into 7administrative divisions, spanning a land area of 347.69 square kilometers. Compared to the rest of the country, Maputo City is notably better equipped with health personnel and facilities. It has 37 health facilities, including 1 quaternary central hospital, 3 secondary general hospitals, and 33 primary health centers—27 urban and 6 rural [ 33 ]. Manhiça District is a rural district in Maputo Province, covering 2,373 square kilometers and located 80 kilometers north of Maputo City, with a population of two hundred thousand [ 34 , 35 ]. Manhiça district has 21 primary rural health centers and health posts and 2 secondary rural, district referral hospitals [ 33 ].

Our study sites, Chamanculo General Hospital in Maputo City and Xinavane Rural Hospital in Manhiça District are both secondary hospitals offering comprehensive maternity care. While Chamanculo General Hospital does not offer C-section services, Xinavane Rural Hospital does. The maternity wards at both hospitals are divided into three areas: admission, delivery, and postpartum [ 31 ]. These areas correspond to the four pause points that the SCC uses to streamline the routine flow of childbirth delivery care: on admission, just before pushing (or C-section), soon after birth, and just before discharge. The birth attendants who participated in our study are essentially MCH nurses with midwifery skills, working 12-hour shifts [ 30 ]. They also rotate across various MCH departments within the hospitals, demonstrating proficiency in family planning, prenatal, intrapartum, and postnatal care, as well as gynecological services. Both hospitals employ a mix of different level MCH nurses, categorized by the extent of their education and training, including elementary (equals to Grade 7), basic (Grade 10), mid-level (Grade 12), and high-level (college-educated) nurses. MCH nurses with higher levels of education are equipped to manage more complex obstetric and gynecological cases, with those at the highest level being qualified to perform C-sections.

The information system in maternity care primarily consists of patient registration forms [ 36 ]. MCH nurses in maternity wards complete comprehensive registration forms for each mother, documenting clinical conditions and information from admission to discharge. These forms capture basic patient information, such as name, age, and national ID number, and clinical information, including gestational age, childbirth procedure and outcome, direct and indirect obstetric morbidity, and newborn conditions. Maternity care also incorporates data collection systems from various specific programs, such as the HIV and malaria programs [ 30 ]. From admission to the postpartum period, MCH nurses log and monitor progress of pregnancy, childbirth, postpartum conditions for mothers and newborns, and their medications.

Different guidelines are employed in different parts of the maternity ward. In general, the admission room personnel have access to guidelines for managing hypertension in pregnancy and sexually transmitted infections in pregnant women such as HIV and syphilis. The delivery room is equipped with guidelines for neonatal resuscitation. The postpartum services have guidelines for managing postpartum hypertension, postpartum infection management, and neonatal sepsis. All rooms follow guidelines for managing maternal bleeding before, during, and after childbirth. The current guidelines are specific to certain procedure or complication but are not integrated as the SCC. There is also no current standardized monitoring or reporting checklist used in the maternity wards.

The hospitals were selected as study sites for focus group discussions (FGDs) and interviews with providers taking into account the distance to the researchers’ office located in Maputo City, their capabilities to perform comprehensive maternity care, and their distinct rural and urban contexts. The inclusion of a diversity of hospitals offered a broad perspective on the varying conditions within Mozambican health facilities.

Study design

To ensure a comprehensive perspective, this qualitative study consists of three types of participants: birth attendants, clinical administrators, and decision-makers. The study conducted four FGDs with twenty-four birth attendants and six individual interviews with clinical administrators from Xinavane Rural Hospital in Manhiça District, Maputo Province, and Chamanculo General Hospital in Maputo City, as well as eight individual interviews with decision-makers at the MoH, the Departments of Public Health for Maputo city and Maputo province, and the Association of Midwives in Mozambique. The interviews and FGDs were guided by the Consolidated Framework for Implementation Research (CFIR) and covered four of five CFIR domains: (a) individual characteristics, (b) intervention (SCC) characteristics, and the facility’s (c) outer settings and (d) inner settings [ 37 ].

Data collection

Participants for this study were recruited using purposive sampling methods, aiming to include individuals with diverse backgrounds who were highly knowledgeable and experienced in following and implementing various policies and clinical guidelines related to childbirth practices and fulfilled the inclusion criteria and could offer valuable insights relevant to our research questions. The recruitment and data collection period took place September 16 th , 2022 to February 10 th , 2023. The FGDs with birth attendants and the interviews with clinical administrators were conducted at secure private offices at the two hospitals. One interview with a decision-maker was conducted via Zoom, while the other interviews with decision-makers took place either at the secure office of the Comité para a Saúde de Moçambique (Mozambique’s Health Committee) in Maputo or at the interviewees’ private offices. The clinical administrators interviewed at each clinical site included those managing MCH care. The clinical administrators also helped the study identified the birth attendants for FGDs. Each focus group comprised five to six birth attendants who met the inclusion criteria: being 18 years or older, having at least one year of experience in maternity care, availability and willingness to participate, fluency in Portuguese, and the ability and capacity to give consent. Similarly, clinical administrators and decision-makers were eligible if they had at least one year of experience managing or monitoring maternity services or MCH programs, were 18 years or older, fluent in Portuguese, available and willing to participate, and capable of giving informed consent. Decision makers were identified through the networks of our local collaborators with the Comité para Saúde de Moçambique and the Mozambique MoH. All participants were approached by a female researcher (AH, DM, or EK) and obtained written consent for participation in the interviews or the FGDs.

To assess the impact of various factors on SCC implementation, we designed the question guides of the FGD and interview based on CFIR. The questions were designed to assess current childbirth practices and infrastructure as well as the feasibility of implementing SCC to improve maternal and perinatal outcomes in Mozambique. The interview and FGD guides were tailored to the roles and responsibilities of the participants (see S2 Text ). We created a pilot FGD guide and tested it to ensure that study participants could adequately contribute to a rich discussion (see S1 Table ). The pilot FGD was conducted at Malhangalene Centro De Saúde (Health Center at Malhangalene) with seven birth attendants from five different health centers who did not work at the two selected clinical sites where formal data collection was to be conducted. The officers at Association of Midwives designated the birth attendants who participated in the pilot FGD. Each of them had rich prenatal-to-postnatal-care work experience from their clinical rotations in the maternity wards. We adjusted the structure and wording of the questions as needed and enhanced the moderating skills of the researchers during pilot [ 38 ].

Prior to data collection, all participants were given hard copies of the WHO SCC at least one day before the interview and FGD to familiarize themselves with its contents. After the interview and FGD, the SCC copies were collected by the researchers to avoid any unintended consequences resulting from the use of the SCC without proper instruction and support. The overall purpose of the SCC and each of its check items were explained to study participants before the FGD and interview. Participants were given opportunity before and after the FGD and interview to ask questions about the SCC and study, and those questions were subsequently addressed by the researchers. This was done to ensure all participants comprehended the content and intended use of the SCC. Participants received compensation for their participation.

Each interview and FGD lasted approximately 60 minutes, and each was scheduled at the convenience of participants, most often during their lunch breaks. All interviews and FGDs were conducted in Portuguese. A researcher (EK or DM) went through the SCC and the consent form verbatim in Portuguese before each interview or FGD and asked if there were any questions related to the study, the SCC, or the consent before the session started. Any questions raised by the participants were addressed accordingly. Participants signed written consent forms before interviews and FGDs. To assure their anonymity, participants were identified with a participant ID instead of their names during data collection and analysis. The interviews for clinical administrator and FGDs for birth attendants were conducted by two qualitative researchers, one of whom (EK) has a Doctor of Medicine degree from the School of Medicine at Eduardo Mondlane University in Mozambique and a Master of Public Health degree from Southern Medical University in China. EK had been working as a physician, district health director, and researcher at MoH since 2014, and she was also responsible for identifying and contacting the hospitals, clinical administrators, and decision-makers. The other researcher (DM) is a local research assistant has a bachelor’s degree in social science from Eduardo Mondlane University in Mozambique and is a qualitative researcher by training. The decision-maker interviews were conducted by EK and AH. AH has a Master of Public Health in Health Policy with formal qualitative study training from Yale School of Public Health in the U.S. The researchers worked in pairs during the interviews and FGDs. One served as the moderator and took detailed notes. The other researcher took comprehensive field notes and was also responsible for timekeeping. The field notes captured the behaviors and nonverbal cues of participants and, as complementary information to facilitate later data coding and analysis, described the physical spaces in which the interviews and FGDs were conducted [ 39 ].

All interviews and FGDs were recorded for later transcription, translation, and data analysis. Within 24 hours after each interview and FGD, the researchers also completed a summary report for each data collection session, including observations, personal reflections, memos, and key takeaways.

The hard copies of the research materials, such as field notes and consent forms, are stored in a locked cabinet in a locked office at Comité para a Saúde de Moçambique, and the electronic data, such as audio recordings and transcripts, were stored in Box, a secure password-protected database authorized by Yale University.

Data analysis

The audio recordings of the interviews and FGDs were uploaded to HappyScribe, a password-protected online software, and then transcribed and translated from Portuguese to English. To ensure their accuracy and integrity, the transcriptions and translations were then carefully reviewed by a bilingual researcher, EK.

The data analysis was performed by a team of three female researchers, AH, DB, and SR, from Yale University with formal qualitative study training. The data from FGDs and clinical administrators were coded and analyzed by AH, DB, and SR, and the data from decision-makers were coded and analyzed by AH and SR. The information in transcripts that might reveal the participant’s identity was removed. The data analysis employed a rigorous deductive thematic method, enabling a thorough and nuanced analysis of the data [ 40 ]. The coding process used a deductive approach, using the pre-established CFIR codebook as a guide [ 37 ]. During the development of the codebook, exemplar quotes, enriched code definitions and descriptions, and detailed inclusion and exclusion criteria were added to the initial CFIR codebook in Microsoft Excel to provide clear guidance for the coding process and contextualize the CFIR codebook for our study.

After developing the codebook, each member of the data analysis team independently coded each transcript using the comment feature in Microsoft Word. Throughout the coding process, the data analysis team met regularly to review and discuss the coded segments line by line and resolve any discrepancies through highly participatory group discussions to achieve consensus and ensure the coding consistency. When the coding was completed in the Microsoft Word, the transcripts were imported to NVivo 14, a qualitative analysis software, and recoded to match the coding in Word. The NVivo was used to enable the retrieval of the coded segments and facilitate the systematic analysis of the codes. The data analysis team also incorporated feedback from the interviews and FGDs moderators (EK and DM) to ensure the interpretations were aligned to the data. Furthermore, the detailed narrative for each code and findings from the coding process were organized according to each of the CFIR domains. Finally, the data analysis team identified common themes across the findings categorized by the CFIR domains. These themes were then categorized into SCC implementation facilitators and barriers.

Ethical statement

This study was approved prior to the start of data collection by the Human Subjects IRB committee at Yale University in the United States in May 2022 (IRB protocol #2000032748) and the Comité Nacional de Bioética para a Saúde in Mozambique (National Committee for Bioethics in Health, CNBS) in September 2022 (IRB protocol #00002657). Prior to collecting data, participants were provided with a consent form. EK went through the consent form in a thorough and word-for-word manner, explaining all aspects of the study, including the participants’ right to choose whether to participate, their ability to withdraw from the study at any point, the procedures in place for safeguarding the confidentiality and anonymity of their information, and the general contents of the FGD and interview. Participants were required to sign the consent forms if they wanted to participate, with one copy provided to them for their own records and another kept as part of the study documentation at the Maputo office of Comité para a Saúde de Moçambique.

Inclusivity in global research

Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research for this study is included in the Supporting Information ( S1 Checklist ).

Conceptual framework

The CFIR examines the implementation environment of an intervention, and how to facilitate its effective implementation through the lens of five domains, (a) intervention characteristics, (b) outer setting, (c) inner setting, (d) individuals’ characteristics, and (e) implementation process [ 37 ]. As this is a formative study to assess the feasibility of SCC implementation, we excluded the implementation process domain as the SCC has not yet been implemented. Among the four domains, we identified eleven constructs that are relevant to our study for analyzing the qualitative data: (a) intervention characteristics (complexity, adaptability, relative advantage, and innovation cost), (b) outer setting (policies and laws, partnerships and connections, and societal pressure), (c) inner setting (compatibility, available resources, and culture), and (d) characteristics of individuals (knowledge and beliefs about the intervention).

Twenty-four birth attendants participated in the FGDs, and six clinical administrators and eight decision-makers took part in the individual interviews. As no new information emerged after the four FGDs and fourteen individual interviews, we considered that information saturation was reached. The duration of FGDs ranged from 39 minutes to 62 minutes, and interview time ranged from 26 minutes to 70 minutes. Detailed sociodemographic characteristics of the participants are presented in Table 1 .

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https://doi.org/10.1371/journal.pgph.0003174.t001

All codes identified from the transcripts were mapped to CFIR constructs. Of the 48 CFIR constructs assessed, eleven were determined to be relevant barriers and/or facilitators to implementing the SCC. Specifically, one CFIR construct addressed facilitators (complexity), and five CFIR constructs addressed barriers (adaptability, relative advantage, innovation cost, available resources, and societal pressure). Six other CFIR constructs addressed both facilitators and barriers (policies and laws, partnerships and connections, compatibility, culture, and knowledge and beliefs about the intervention). The study findings were organized by themes below, and Table 2 linked the barriers and facilitators of the SCC implementation to specific CFIR constructs.

https://doi.org/10.1371/journal.pgph.0003174.t002

Facilitators

The scc is simple and easy to understand..

When participants were asked about the complexity of the SCC, they agreed that the content and format of the checklist were easy to understand.

“I don’t think it is complicated at all. It just has basic aspects of everyday life in a maternity ward, or the day-to-day life of a midwife, or a nurse, so I don’t think it’s complicated. It’s direct, it has very concrete aspects.” (Decision-maker 5)

The SCC aligned with the national maternal and child health agenda.

The participants stated that the current MoH guidelines and efforts were consistent and reflected in the SCC objectives, indicating that the SCC implementation aligned with the national maternal and child health agenda.

“In general, one (SCC) is applying what are practices according to the MoH guideline, which is humanized childbirth or humanization of childbirth… All nurses have this orientation.” (Clinical Administrator 5)

Participants mentioned that strong support and commitment from both the local community and public health leaders about safe childbirth were crucial, as they can significantly contribute to spreading information on MCH and motivate clinics and birth attendants to engage in the SCC implementation effort.

“We, in the community, have the community leaders, maternal health nursing component, and the traditional midwives. They help information dissemination of the maternal and child health package… We will be able to involve them, to know that there is a checklist… so that they can help the dissemination of information.” (Decision-maker 4)

Furthermore, decision-makers emphasized that the MoH undertook regular supervision visits, offered technical support, and conducted in-service training at clinics. These initiatives are designed to ensure guidelines compliance and improve service quality in maternity wards. Such efforts aligned with the objectives of the SCC and may aid in its effective implementation.

“We do the monitoring of the activities, supervision… both scheduled supervisions and surprise visits. We make surprise visits to maternity hospitals, mainly to check if in fact they are doing their job well… We also reinforce it with some in-service training. When we get there, in these supervisions, we also explain: ‘Look, you are not doing it right here.’ We correct what is good practice and follow up on the needs.” (Decision-maker 1)

Participants had positive beliefs about the SCC.

During the interviews and FGDs, the participants displayed a strong understanding, wealth of knowledge, and a high level of professionalism and dedication to improving the quality of childbirth practices. They were open to updating their knowledge using the SCC and acknowledged the importance of continuously learning and keeping themselves informed.

“Science is dynamic. There are things that are being abolished and things that are being introduced. So, I try to say you should implement this study, while one thing or another could be abolished, so as we are here the council, we are here today to learn…. Let’s give progress to this study.” (Birth Attendant 8)

Moreover, participants expressed confidence that the implementation of SCC would lead to positive changes in current practices and result in improved quality of maternity services.

“I think that the list has a format that goes according to what we are talking about, because what we need is a standard procedure for the teams. Then, for the complications, we will have more trained people, but we also need team with a minimum standard procedure, and the list is simple. It is a list that reduces the time of work or procedure of the colleague… I find the list simple and sufficient.” (Decision-maker 7)

The SCC was viewed as redundant.

Participants expressed that the SCC did not offer a significant advantage over their current work routine, viewing it as an additional form to fill out and adding to the workload of birth attendants.

“It would be one more instrument. It would be a repetition of what we already do… All these flowcharts that we have already exist. And that is exactly what we do. And it looks like we don’t read it, because this, because that, but no. We already do that … We end up having less time to do our activities, to exercise the technique. We stay longer, we have [to] read and write, which doesn’t help us much either. It is very tiring.” (Birth Attendant 3) “I don’t think that the Checklist, by itself, will meet the needs… what will happen is this list will be one more paper in the maternity ward… The form alone is not going to change anything. It is one more piece of paper, it is going to be one more tool. As I said, the current guideline already recommends many of these questions, and they are in the form. What do we do? We fill it out, fill it out, fill it out.” (Decision-maker 6)

The SCC might be incompatible with the current workflows.

The concepts and practices outlined in the SCC were found to be mostly consistent with current practices in the maternity unit according to the participants. Filling out the SCC itself, however, is likely to impede existing workflows due to human resource shortages and time constraints. Participants expressed concerns about how to allocate time for other clinical activities and fill out the SCC, as there may be competing priorities.

“Because of the overload of work, one or another thing ends up slipping away… We have gynecology, maternity, c-sections, pathological pregnancy, gynecology, admission, delivery room, it’s for one nurse… So, everything that happens there ends up exhausting your knowledge, and your strength, you don’t know what to do…. It’s not because she is unwelcome [the SCC], she is welcome, yes. But treating the person himself, the work, it becomes difficult to follow the form.” (Birth Attendant 9)

Moreover, participants expressed concern about the workload related to paperwork. They already had a significant amount of paperwork to fill out, and the addition of SCC might increase their workload. Some participants suggested simplifying the current paperwork instead of introducing a new one.

“It is complicated because we already have many instruments. If the list doesn’t come to remove anything, it comes to add, it’s another job… Now, if the list comes and reduces the work for us, and summarizes a lot of things, it is welcome. If it is to add to it, it will not make us comfortable.” (Clinical Administrator 4)

The SCC needs to be better aligned with the context.

Although the SCC was viewed as simple and easy to understand, participants voiced the need to adapt it to the local context. Participants proposed multiple adaptations to integrate the SCC into their work routines and contexts, enhancing its implementation feasibility. These adaptations included transforming the SCC into a pocketbook rather than adding it to existing paperwork, displaying it as a wall poster, incorporating a section explaining incomplete practices, using it to evaluate supply availability, and merging it with existing tools like the patient clinical registration form, which includes medical history and diagnoses.

“My suggestion would be that it should be in a format like these HIV flowcharts, for example. You don’t make us waste time even opening a document and looking for how to do it. Then nail it to the wall…the person looks, sees the explanations and does it. It is easier to do than in the form of a list.” (Clinical Administrator 2) “Or maybe one could think of a decentralized instrument, which could perhaps feed into another instrument already at the central level… If we had an instrument that helps us to check what is the quality of the work of our maternity ward… And maybe to send the information to the central level as well, to see what is happening, what is failing, which is to take the proper precautions.” (Clinical Administrator 5)

Inadequate external support may hinder SCC implementation.

Participants emphasized that the external financial support from the MoH to maternal health care was inadequate, and the assistance from funders and partners was distributed unevenly across the country, often focused on specific diseases in a vertical manner. This could impede the adoption and implementation of the SCC, given that maternal health care is currently underfunded and not given priority.

“The financial allocation for the reduction of maternal and child mortality in a direct way is minimal, is reduced, and is ineffective. We have a maternal and child health plan in a year that cannot meet 50% of the needs… The use of external funds, which is far from the Paris Declaration, we don’t have much flexibility of funds to decide where they are allocated. The care area is underfunded, and it is the area that we should improve. We have pillars that are necessary [to be improved, including] educating how the delivery has to be, pregnancy care, the significance of various stages of pregnancy, labor expectations, pain management, and practices." (Decision-maker 7)

With the specific pillars the decision-maker highlighted also being key elements in the SCC, the current lack of financial support for maternal and child health care could signal potential challenges in implementing the SCC.

Resource shortfalls may impede SCC implementation Resource shortfalls may impede SCC implementation.

A major barrier to the SCC implementation is the limited availability of resources, including human resources, materials, physical space, and professional training. Despite the perceived benefits of SCC, the severe shortage of resources makes it challenging to successfully implement the SCC in clinical settings.

“What we need in Mozambique, in fact, is more equipped rooms, more spacious rooms, because our infrastructure sometimes does not create these types of conditions for a well-designed guideline. The strategies are well designed, but our conditions don’t help us, they don’t favor us having this model birth (SCC) that we are talking about, which would be better.” (Decision-maker 3)

Notably, participants expressed concern that the implementation of SCC would further increase their overwhelming workload as there is typically only one nurse per shift in the maternity unit, responsible for caring for both mothers and newborns. The already serious staff shortage could not accommodate the addition of another instrument that might increase the provider burnout. Allocating scarce time to complete the SCC would further increase staff workload.

“The implementation of the list is not bad. But as we were just saying… the lack of human resources, I think that this list will be more of an overload, an extra work, where the staff at that moment are few… But the list is not bad. It is very good, it helps. It is the moment when someone can forget something, looking here, sees that here is something that can be done or should be done. But looking at the work you already have in the maternity ward, it’s a lot. There are many documents to be filled out. One more document, it’s more overload.” (Birth Attendant 18) “We would feel overwhelmed. [The nurse] couldn’t fill out… and she is going to be overload. How is it? She will even ask herself, ‘but can’t you see? Because I am all alone.’” (Birth Attendant 17)

The scarcity of essential birth supplies in the maternity ward posed another significant barrier to implementing the SCC and achieve its purpose to enhance the quality of childbirth practices.

“For the maternity case, we are missing too many antihypertensives. Just talk about methyldopa, hydralazine, dihydralazine… and this has made our work very difficult.” (Clinical Administrator 5) “There are no gloves. How will it go well? How will you take care of yourself? How will you comply with what the document [SCC] asks for?” (Clinical Administrator 1)

Meanwhile, the participants highlighted the importance of professional training for successful SCC implementation and requested refresher training to improve their knowledge and skills.

“I think that if the people who are [going] to use the checklist are not very well trained, they can have a complication because it [the SCC] can be filled out not in the same standard way. The training of the people who are going to use the form itself needs to be standardized.” (Clinical Administrator 4)

Furthermore, the cost of the SCC implementation poses another challenge. The health facilities in Mozambique have very limited resources, and the costs of reproducing, distributing, storing, and completing the SCC, including expenses such as printers, paper, and storage space, could add an additional financial burden on the clinics.

“The list is produced, and then it is the health unit’s responsibility to reproduce it. And that doesn’t go very far, because we will see that the health unit doesn’t have the capacity to reproduce the form itself…It is already difficult for the health unit to continue because they are not all able to multiply their own records.” (Clinical Administrator 4)

Low motivation and societal pressures deter providers from adopting SCC.

Participants indicated that their existing workload, particularly with paperwork and completing instruments, was already overwhelming. They expressed concerns about their ability to properly fill out additional forms, suggesting that introducing a new instrument could be daunting.

“We get blinded in front of a document. Many times, we get scared just by looking at the document. Do this, we have to fill it out like this. Sometimes we fill it out, but not properly as it should be.” (Birth Attendant 10) “Whenever we get a new instrument, there is resistance in change, because at some point, the nurses have to give their reasons because they have too many instruments to be able to fill out, to be able to check. When more than one instrument arrives, they get a little tired, a little angry, because we have many books to fill in.” (Decision-maker1)

Additionally, some participants expressed concerns that failure to fill out the SCC could result in penalties or other negative consequences.

“It would be possible [to implement the SCC]. It would help some, but it could also penalize us for things that are not our level of competence to resolve, such as the issue of lack of medicines, lack of running water, at some point in the anesthesia machine, a shortage of operating room staff.” (Clinical Administrator 5)

Moreover, many birth attendants reported experiencing stigma and pressure from mass media, local community, and patients, which further limited their motivation to adopt another instrument like SCC and improve the quality of the maternal and child health services.

The participants expressed that the social recognition of birth attendants was low, and this lack of recognition was a demoralizing factor in their work. Despite the birth attendants’ strong desire to improve their work and adopt SCC, they felt that their efforts were not valued or recognized by the community.

“Because if we look at the media, they are against us. Just for someone to be born outside, we are already on television. But if I attend childbirth outside without gloves to help, I won’t be on television. But if someone is born outside, even five meters from the hospital, we are going to be smeared with all of this. ‘Chamanculo is negligent, there was no emergency room.’ So, motivation factor.” (Birth Attendant 15)

There were instances in which the companions or patients complained the practices of the birth attendants, resulting in the spreading of negative comments about the birth attendants in the community, further diminishing their motivation to work.

“Even being a woman, a companion [of the delivery mother] doesn’t understand what happens inside the maternity ward. Even the techniques that the nurse will perform, she thinks you’re mistreating that person… She starts talking bad about us in the community.” (Birth Attendant 6)

Main findings and interpretation

This formative qualitative study sought to identify potential facilitators and barriers to implementing the SCC in the context of the childbirth practices and conditions in Mozambique at the time this study was conducted. The study explored the feasibility of SCC implementation by assessing the initial knowledge and attitudes of a diverse group of stakeholders from various professional backgrounds.

The barriers and facilitators identified in our study agree with most of the findings from the countries where the SCC had been tested before [ 14 , 21 , 26 , 27 , 41 ]. The common facilitators of SCC use were related to the checklist itself, as it’s easy to complete and acts as a useful reminder for essential childbirth practices that aligned with the national and local guidelines [ 14 ]. The major barriers were linked to local challenges, including insufficient material and human resources, inadequate training, perceptions of increased workload associated with the SCC use, lack of staff motivation to use SCC, and an underfunded MCH care [ 14 , 21 , 27 , 41 , 42 ].

In Mozambique, due primarily to the structural challenges of the overall health system, the implementation of SCC faces multiple obstacles. Support for MCH care from the MoH and external funders was found to be inadequate and not given priority, with resource distribution often focused on specific diseases through a vertical approach. This lack of funding for maternal care might further limit the resources available for adopting SCC and hindered the implementation of quality, evidence-based delivery practices required by SCC. Clinics in our study commonly faced shortages of essential medicines, equipment, and materials needed for critical childbirth practices. Additionally, the costs associated with reproducing, distributing, storing, and completing the SCC imposed an extra financial burden on the already under-resourced maternity services in the clinics. Moreover, given that there was often only one birth attendant per shift in the maternity ward, implementing and completing the SCC may have competed with other clinical activities for the limited time, resources, and attention of the birth attendant. As a result, birth attendants viewed the SCC as redundant, feeling it added to their workload without offering significant advantages over their current practices. They also found the prospect of introducing another instrument daunting, given the already substantial paperwork in the clinics. Additionally, there was concern that failing to complete the SCC could lead to penalties.

Meanwhile, mothers’ mistrust and perceived poor quality of care have led to blame directed at birth attendants, which may have contributed to their low motivation. Negative comments from the community further undermine the birth attendants’ social recognition and increase societal pressure on them. Our study participants highlighted poor morale, weak motivation, and low recognition among the primary reasons for their reluctance to adopt another protocol like the SCC, in the context of their already overwhelming workload. These barriers need to be addressed to facilitate the SCC implementation in Mozambique.

We recognized that implementing the SCC in our study context involves many interacting factors that potentially reinforce each other within a dynamic system. Therefore, we hypothesized that there were negative feedback loops that hindered the health system’s ability to implement the SCC. Informed by our findings we further hypothesize that these feedback loops were likely to be (a) a weak MCH care system, (b) limited availability of resources, (c) heavy birth attendant workload, and (d) low motivation among birth attendants ( Fig 1 ). Our hypotheses are consistent with findings from a previous study conducted in Nampula Mozambique seeking to understand how to improve breast feeding counseling through the health system [ 43 ], highlighting the fact that our findings have implications beyond just the SCC.

https://doi.org/10.1371/journal.pgph.0003174.g001

Despite these obstacles, many birth attendants remained committed to improving the quality of childbirth practices and adopting SCC. They recognized that the SCC aligned with the national MCH goals and the need to continue educating themselves. Birth attendants did not express the need for pay-for-performance for filling out SCC but suggested that allocating more financial resources towards creating better working conditions and strengthening the healthcare system would be helpful. Moreover, participants suggested modifying the format of SCC, such as displaying it as a poster in the maternity ward or integrating it into existing tools like the patient clinical registration form. This would help contextualize SCC’s use, better integrate it into the health providers work routines and facilitate its implementation. However, it is possible that altering the use and format of the SCC might contribute to potential changes in its original purpose and affect its efficacy.

Limitations and strengths.

This study has several limitations. Firstly, since participants lacked real-life experience in SCC implementation, the barriers and facilitators identified were not directly informed by their experience of using the SCC. Moreover, without implementing the SCC, this formative research study was not able to assess the actual implementation process domain of the CFIR or identify potential effective activities utilized in the SCC implementation [ 37 ]. However, we provided the SCC to participants at least one day before the interviews and FGDs and explained the purposes of the SCC iteratively before and throughout the interviews and FGDs to facilitate their understanding of its content and use. While we could not confirm whether participants had read the SCC beforehand, we took steps to ensure their understanding of its purpose and checklist items. Before each FGD and interview, participants were explained the purpose of the SCC and each of its checklist items. Researchers addressed the participants’ questions to ensure that they all understood the content and intended use of the SCC before proceeding with, and during and after the interviews and FGDs were conducted.

Moreover, it’s important to highlight that in our study participants were deliberately chosen for their extensive knowledge and experience in adhering to and implementing various clinical guidelines related to childbirth practices and policies. As frontline health workers and policymakers, they had extensive familiarity with the objective and integrated content of the SCC. During the FGDs and interviews, they indeed indicated that although the SCC might present a new format as a clinical checklist, the content was familiar to them. Additionally, based on their experience they were able to identify specific items in the SCC that they felt would be challenging and provided substantive feedback on these items during the discussions.

Secondly, we sampled one rural and one urban hospital in Maputo Province and Maputo City, aiming to represent varying conditions in health facilities. Nonetheless, our sample may not fully capture the reality across Mozambique, given the substantial differences in health care quality and access across the country, the external validity of our findings must be interpreted with caution. Moving forward, future SCC studies in Mozambique should include various levels and types of health facilities, including primary health centers, from different regions of the country.

Thirdly, while we hypothesized the presence of several negative feedback loops involving barriers from system-level to individual-level that may make SCC implementation challenging, we acknowledge that this hypothesis needs to be confirmed through further research as causal relationships cannot be established through a qualitative study. We further recognize that the hypothesized feedback loops are an oversimplified representation of barriers to SCC implementation. Further research will also be needed to understand how to counteract negative with positive feedback loops to enable SCC implementation in the context of under-resourced maternity healthcare systems.

Lastly, we fully acknowledge that it will be crucial to include the views of women and the community in the co-design of the SCC implementation process in Mozambique. As an initial formative study, we chose to concentrate first on the perspectives of birth attendants, clinical administrators, and decision-makers in Mozambique, aligning with the clinical context where the SCC is intended to be applied. Future community-engaged co-design studies conducted by our team will incorporate the voices of local women and the community to ensure comprehensive and inclusive insights.

Despite the limitations, this study has several strengths. While our findings confirm findings previously reported in other countries, this study stands out as the sole formative qualitative study that was conducted prior to actual SCC implementation and the first SCC study conducted in Mozambique. Our approach aligns closely with the WHO Safe Childbirth Checklist Implementation Guide [ 44 ], emphasizing the necessity of assessing available resources and current practices prior to large-scale implementation to determine how the SCC can be optimally employed and what prerequisites must be met for its success.

Conducting this study before SCC implementation offers several benefits. This formative study reflects a commitment to ensuring that SCC implementation aligns with and addresses the country’s specific needs. As reported by a previous study, SCC implementation might increase the workload and frustration of birth attendants [ 21 ]. Ignoring this clear finding confirmed in our study could inadvertently generate unintended consequences within local communities and the MCH care system in Mozambique.

Moreover, our study was carried out in close collaboration with Mozambique’s MoH based on the principles of mutual respect and benefit, equitable communication, and productive dialogue between the global health research team and the local partners, with a commitment to reporting our findings to local healthcare leadership [ 45 ]. The findings of this study have been presented to the decision-makers and researchers in Mozambique and will be further disseminated in the country to assist the MoH in determining the next steps for SCC implementation. We expect for our findings to support a co-design phase of an initiative to implement the SCC in Mozambique.

Implications.

Our study identified severe health care systems resource shortage as a key barrier to the SCC implementation in Mozambique, emphasizing the need to reconsider the focus of MCH studies and research methods used. Unlike the typical practice of conducting pre-post-implementation studies or randomized controlled trials (RCTs) to investigate facilitators and barriers for SCC implementation, our study shows that a proactive pre-implementation assessment can provide equally important contextual insights. Furthermore, conducting pre-implementation assessments could inform resource allocation strategies to address critical gaps in human and material resources for the SCC implementation with the ultimate goal of strengthening the overall MCH care system.

Furthermore, given that numerous barriers to SCC implementation are fundamentally linked to the shortcomings of Mozambique’s healthcare system, we call for future funders and partners shifting their focus from vertical funding to initiatives that prioritize the provision of essential materials, human resources, and professional training in primary care. Moreover, recognizing that there is no one-size-fits-all model for SCC implementation due to various contexts, future implementation research should include different types of health facilities and various levels of healthcare systems across Mozambique. Future research should take into account what we have learned from our study in Maputo City and Maputo Province and determine the optimal complementary intervention packages to adapt SCC implementation strategies to the country’s unique settings [ 42 ], taking the voices of women and communities fully into account.

In conclusion, our innovative study has played a crucial role in empowering local providers by listening to their voices and engaging them in the decision-making process for the implementation of the SCC in Mozambique. Their contributions have highlighted the urgent need for improving the quality of MCH care and enhancing the capacity of the health system in the country. Moreover, our study has identified various key factors that are vital for the successful implementation of the SCC, which include ensuring the availability of adequate human and material resources, providing comprehensive professional training, adapting the SCC contextually, maintaining strong political commitment, and garnering support from equitable partnerships. Lastly, we call for future research to undertake a holistic evaluation of the local context prior to the implementation of the SCC, thereby promoting decolonized global health research and practice and ensuring that interventions are contextually relevant and culturally sensitive.

Supporting information

S1 text. who safe childbirth checklist..

https://doi.org/10.1371/journal.pgph.0003174.s001

S2 Text. Question guides for FGDs and interviews.

https://doi.org/10.1371/journal.pgph.0003174.s002

S1 Table. Codebook and question guide for pilot FGD.

https://doi.org/10.1371/journal.pgph.0003174.s003

S1 Checklist. PLOS questionnaire on inclusivity in global research.

https://doi.org/10.1371/journal.pgph.0003174.s004

Acknowledgments

The authors would like to thank Dr. Lucian J. Davis, Dr. Ashely K. Hagaman, and the staff at Comité para a Saúde de Moçambique for their generous guidance and support.

• 1. SDG Target 3.1 [Internet]. World Health Organization; [cited 2023 Mar 5]. https://www.who.int/data/gho/data/themes/topics/sdg-target-3-1-maternal-mortality
• 2. Recommendations for Clinical Practice of Emergency Obstetrical and Neonatal Care in Africa. Brazzaville: WHO Regional Office for Africa; 2022.
• 3. Plano Estratégico do Sector da Saúde (PESS) 2014–2019. Direcção de Planificação e Cooperação. MINISTÉRIO DA SAÚDE; 2013.
• 4. Programa Quinquenal do Governo: 2020–2024. REPÚBLICA DE MOÇAMBIQUE; 2020.
• 5. Maternal mortality ratio (modeled estimate, per 100,000 live births)—Mozambique. World Development Indicators. [Internet]. The World Bank Group; 2019. https://data.worldbank.org/indicator/SH.STA.MMRT?locations=MZ
• 6. Mortality rate, neonatal (per 1,000 live births)—Mozambique. World Development Indicators. [Internet]. The World Bank Group; 2020 [cited 2023 Mar 5]. https://data.worldbank.org/indicator/SH.DYN.NMRT?locations=MZ
• 7. WHO Global Health Observatory data repository [Internet]. 2023. https://apps.who.int/gho/data/view.main.STILLBIRTHv?lang=en
• 8. Mortality rate, neonatal (per 1,000 live births)—Sub-Saharan Africa. World Development Indicators. [Internet]. The World Bank Group; https://data.worldbank.org/indicator/SH.DYN.NMRT?locations=ZG
• 9. UNICEF Data: Stillbirth [Internet]. https://data.unicef.org/topic/child-survival/stillbirths/
• 10. UNICEF Data: Maternal mortality [Internet]. https://data.unicef.org/topic/maternal-health/maternal-mortality/#:~:text=Levels%20of%20maternal%20mortality&text=There%20are%20large%20inequalities%20in,in%20Australia%20and%20New%20Zealand .
• 11. Departamento de Saúde da Mulher e da Criança: Relatório Anual, 2020. MINISTÉRIO DA SAÚDE, DIRECÇÃO NACIONAL DE SAÚDE PÚBLICA; 2020.
• View Article
• PubMed/NCBI
• Google Scholar
• 13. WHO safe childbirth checklist implementation guide: improving the quality of facility-based delivery for mothers and newborns. World Health Organization; 2015.
• 26. World Health Organization. WHO safe childbirth checklist collaboration evaluation report [Internet]. Geneva: World Health Organization; 2017 [cited 2023 Sep 4]. 61 p. https://apps.who.int/iris/handle/10665/259953
• 28. UNICEF MOZAMBIQUE 2022–2026: A strategic partnership for every child. UNICEF; 2023 Sep.
• 29. Country programme document for Mozambique (2022–2026). Executive Board of the United Nations Development Programme, the United Nations Population Fund and the United Nations Office for Project Services; 2021 Dec 8.
• 32. DIVULGAÇÃO OS DADOS DEFINITIVOS IV RGPH 2017. Instituto Nacional de Estatística; 2017 Dec.
• 34. Perfil do Distrito da Manhiça. Instituto Nacional de Estatística; 2005.
• 36. Livro de Registro de Maternidade (MOD-SIS-BO3). Ministério da Saúde. República de Moçambique.
• 44. World Health Organization. WHO safe childbirth checklist implementation guide: improving the quality of facility-based delivery for mothers and newborns [Internet]. Geneva: World Health Organization; 2015 [cited 2023 Sep 10]. 61 p. https://apps.who.int/iris/handle/10665/199177