* difference significant (p < 0.05)
Table Table3 3 shows descriptive results of element scores displayed per type of note. What can be observed from the data in Table Table3 3 is that for structured documentation, the standard deviation decreases in most elements scores, indicating the variability in quality seems to be lower in structured notes. Furthermore, when comparing the grand mean score for IOC and FUC notes separately, an increase for both types of notes was found (Fig. 1 ). IOC Qnote score increased by 14.9 (95% CI 11.3–18.5) points from 67.3 to 82.3. FUC Qnote score increased by 10.8 (95% CI 4.6–17.0) from 61.3 to 72.1.
Descriptive results of Qnote element scores, per note type
Chief complaints | 89,4 | (22,2) | 97,2 | (11,5) | 78,6 | (30,2) | 89,4 | (23,8) |
HPI | 87,4 | (27,7) | 97,4 | (8,6) | 55,8 | (46,4) | 76,7 | (36,3) |
Problem list | 33,8 | (46,6) | 46,5 | (49,0) | 12,7 | (33,1) | 31,5 | (45,8) |
Past medical history | 73,7 | (41,5) | 85,2 | (31,6) | 4,7 | (19,1) | 8,0 | (26,6) |
Medications | 29,5 | (45,3) | 42,0 | (49,5) | * | |||
Adverse reactions | 25,6 | (40,0) | 84,7 | (31,1) | * | |||
Social and family history | 72,5 | (36,2) | 88,3 | (19,4) | * | |||
Physicial findings | 87,3 | (15,5) | 87,0 | (16,4) | 78,2 | (26,5) | 83,6 | (20,6) |
Assessment | 83,3 | (20,6) | 88,3 | (18,7) | 65,8 | (39,3) | 83,6 | (23,5) |
Plan of Care | 80,1 | (25,1) | 89,6 | (17,3) | 69,3 | (41,0) | 69,9 | (43,4) |
Follow-up information | 63,9 | (32,1) | 88,0 | (22,0) | 81,0 | (27,9) | 85,7 | (27,1) |
Grand Mean | 67,4 | (12,6) | 82,3 | (8,7) | 61,3 | (25,4) | 72,1 | (20,2) |
* grey marked elements were not evaluated for this note because these elements were considered not relevant in this type of consultation
Boxplot of grand mean score per note type
Subsequently, analysis was conducted on data from both centers separately to determine whether structured documentation led to increased quality in both centers. In center B, an increase of 14.59 was found (95% CI 7.22–21.96) in IOC note quality, and a 16.36 point increase (95% CI 8.99–23.73) in FUC note quality was found. A significant improvement in IOC Qnote score by 15.10 (95% CI 8.26–22.10) was observed in center A. The 5.3 point increase in FUC note quality was not statistically significant (95% CI -1.61–12.14).
Analysis of secondary outcome measures showed a significant increase in note length for structured documentation in both note types. IOC notes increased from 442.1 to 639.6 words, with a mean difference of 197.5 (95% CI 146.9–248.1), translating to a 44.7% increase. A significant 53.3% increase was found in FUC notes, increasing with 46.5 words (95% CI 31.7–61.2) from 86.9 to 133.4. To evaluate whether this increase in note length led to unnecessary long notes containing excessive non-essential information, all scores for a given component were averaged. For example, the component concise was used to rate 9 of the 11 elements used to rate a note. The mean of all conciseness scores was calculated to get an overall indication of the conciseness of the note. Table Table4 4 shows the difference in mean component scores. As can be seen from the data in Table Table4, 4 , the mean conciseness score, indicating whether note elements were focused and brief, increased significantly. Furthermore, the mean clearness score, indicating whether note elements were understandable to clinicians, also increased significantly.
Mean component score difference between unstructured and structured documentation
| |||
---|---|---|---|
Sufficient information (7) | Enough information for purpose | +14.3 (10.2 – 18.4) | < 0.001* |
Concise (9) | Focused and brief, not redundant | +10.7 (6.5 – 14.9) | < 0.001* |
Clear (8) | Understandable to clinicians | +14.8 (10.6 – 18.9) | 0.009* |
Organized (3) | Properly grouped | +14.5 (7.8 – 21.2) | < 0.001* |
Complete (3) | Adresses the issue | +7.9 (1.61 – 14.3) | 0.014* |
Ordered (1) | Order of clinical importance | +16.2 (4.5 – 27.9) | 0.007* |
Current (3) | Up-to-date | +24.5 (17.3 – 31.7) | < 0.001* |
When analyzing the scores of the general instrument that rated the notes on a scale of one to ten, a significant increase in documentation quality was also found. Mean scores increased from 6.83 to 7.52, which was an 0.68 increase (95% CI 0.44–0.94).
The study offers some important insights into the impact of increased structured and standardized documentation on EHR note quality in outpatient care. In this retrospective multicenter study, our results show that structured documentation is associated with higher quality documentation. In summary, our results show a 20.0% increase measured on a 0–100 scale. Furthermore, results showed that structured notes were significantly longer than unstructured notes, but were more concise nevertheless.
This study showed an overall increase in documentation quality after the implementation of structured and standardized recording. In 8 of the 11 elements measured with the Qnote instrument, a significant increase in quality was found. This result may be explained by the fact that relevant elements and items that have to be documented are presented to the health care provider in an intuitive, uniform way. Therefore, clinicians are less likely to forget certain elements and items within the note. Furthermore, repeatedly recording in the same format ensures the physician is trained to record properly and completely. The medication element showed a minor, insignificant increase. This might be because medications were not included in notes in one center and therefore did not contribute to the observed results on this element. Additionally, minor, insignificant increases were found in physical examination and plan of care. This could be explained by the fact that the score for these elements was already high in unstructured documentation.
A recent study found variation in the quality of documentation between healthcare providers [ 9 ]. This variation could lead to inefficient documentation and the risk of patient harm from missed or misinterpreted information. Therefore, reducing this variability may also be considered relevant. The descriptive data on element scores in this study showed a trend indicating that the variation in documentation quality decreases when using structured documentation. However, some elements still showed significant variation. Therefore, implementing solutions that reduce variation in documentation quality between encounters and healthcare providers should be encouraged.
In addition, when the notes were analyzed differentiated by center, a significant increase in the quality of IOC notes was observed. This was also the case for follow-up notes in one of the two centers. This supports the conclusion that structured and standardized recording increases documentation quality, independent of a specific center or EHR vendor.
The results also show notes were longer when structured documentation was used. This could be because structured documentation contributes to including all relevant elements, or because health care providers are more reliant on CIT. CIT can be a problem if it leads to unnecessary, unorganized, or unclear information in a note and distracts the reader from the essential information buried within the note. This is known as note bloat. When considering the results of this study, there is no evidence that the longer notes were the result of note bloat. Firstly, an increase in quality in almost all elements where CIT is mainly used (problem list, past medical history, adverse reaction, social and family history) was observed. Secondly, the analysis on components used to assess the individual elements showed significant increases in clearness and conciseness. Therefore, it is safe to assume that in this study, the longer notes were not associated with note bloat and are most likely the result of more complete, and therefore higher quality, documentation.
The reports in the literature to date have mainly focused on the effect of electronic documentation versus handwritten documentation. Some studies have shown a perceived decrease in quality after implementing EHRs, identifying copy-paste functions (CPF) and note clutter as the main reasons for this quality decrease [ 17 ]. Others claim that EHRs increase note quality compared to manual recording in inpatient and outpatient care [ 11 – 13 , 18 ]. A small number of studies have evaluated semi-structured templates that mainly use free-text documentation, comparing them to traditional templates or fully unstructured free-text notes. A small (n = 36) trial comparing outpatient notes written using a traditional template with an optimized template found mixed results, with no difference in overall quality [ 19 ]. However, the intervention notes were inferior in accuracy and usefulness, although better organized. Another study evaluating a quality improvement project to improve clinical documentation quality found no increase in quality [ 20 ]. A third, larger study did find a significant increase in inpatient documentation quality using a semi-structured template [ 21 ]. The abovementioned studies indicate that further research on this topic is warranted. However, our findings show compelling evidence that structured documentation can improve documentation quality.
This study has several strengths. This is the first study to use a validated measure instrument for outpatient notes to examine the impact of structured and standardized recording on outpatient note quality. Given the rising demand for reuse and exchange of healthcare data, structured and standardized data recording will become increasingly important. This study proves that structured documentation can also improve the quality of EHR notes. Furthermore, the increase in quality was found in two centers with different EHRs. These factors contribute to the generalizability of the results.
Another strength of this study is the method used to assess the quality of the notes. Of the instruments available in the literature that are used to assess the quality of documentation, most focus on the absence of data or only assess the global quality of the note, such as the PDSI-9 [ 22 ]. However, the Qnote instrument is based on a qualitative study in which relevant elements of an outpatient clinical note were identified [ 23 ]. Therefore, it is possible to rate the quality of all note elements independently and subsequently calculate a total score. This structured approach is likely to be more objective than other, more general rating instruments. Besides, rating elements individually benefit from being able to identify specific deficits in note quality. Because of this, improving the quality of clinical EHR notes can be conducted in a more targeted and effective way.
This study also has some limitations. Firstly, the main limitation of the retrospective nature of this study is that a causal relationship between the implementation of structured and standardized documenting cannot be established with certainty. In one center, the interval between the two study periods was several years. Therefore, the influence of other factors cannot be eliminated. In the other center, the interval between study periods is shorter, making it highly likely that implementing the standardized care pathway with structured documentation is the primary reason for the increase in note quality. Moreover, analyzing the data differentiated by center resulted in similar outcomes. Secondly, the Qnote instrument has been validated on a population of diabetic patients and not for oncological patients. However, the elements used are general and not disease- or setting-specific. Moreover, the general score given by the raters in this study showed similar or marginally lower scores than the Qnote instrument. This conclusion was also stated in the initial Qnote validation study [ 16 ]. Lastly, due to the visual similarity of structured and standardized notes, the complete blinding of study notes for raters was impossible. This might have led to an unconscious bias. However, the risk was minimized by recruiting note raters employed at another hospital.
The findings of this study support the assumption that structured documentation positively influences documentation quality. This is an important finding, given that the need for structured documentation will only increase in the near future because structured data is key in enabling the reuse of healthcare data. Data reuse will become increasingly important in health care, for various purposes, such as automated quality measurement, information exchange when referring patients to other health care centers, and less time-consuming data collection methods for scientific research. Furthermore, the use and implementation of decision support tools also require structured recording of healthcare data. The abovementioned applications of data reuse in healthcare can lead to increased efficiency and quality of healthcare. Nevertheless, there could be a concern that as data reuse becomes more important, healthcare providers are required to capture more data while providing care. This, in turn, might lead to an increased administrative burden. This should be avoided, as healthcare providers are unlikely to accept a documentation method that adds a significant burden to their workload [ 24 ]. Efforts should be made to to implement structured documentation methods within EHRs to enable data reuse while reducing the administrative burden. The results of this study raise further questions about the benefits and pitfalls of structured documentation systems, on which future studies should focus. These include the effect of the structured documentation systems on documentation time and effort, how physicians' perceptions regarding the documentation process and the EHR are influenced, and how these factors affect adoption, and how these factors affect adoption. As a result, we have started another study to answer such questions.
This study demonstrated that structured and standardized recording led to an increase in the quality of notes in the EHR. Additionally, a significant increase in note length was found. Moreover, the results showed that the longer notes were also considered more clear and concise. Considering the benefits of structured data recording in terms of data reuse, it is recommended to implement structured and standardized documentation into the EHR.
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Jim's Computer Science Topics – Commenting Your Code : A professor, H. James de St. Germain, in the School of Computing, University of Utah created these materials for his computer science course students. The programming languages used in examples are Matlab, C, and Actionscript.
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R Markdown: The Definitive Guide : A book written by the creator of R Markdown package, Yihui Xie . A good reference when writing an R Markdown document.
R Markdown chapter in R for Data Science: An R Markdown chapter in the R for Data Science book, written by Hadley Wickham and Garrett Grolemund.
Introduction to R Markdown : A section about R Markdown in Reproducible Research course created by National Bioinformatics Infrastructure Sweden (NBIS) .
An R and R Markdown Tutorial : A step-by-step instruction to create an R project and an R Markdown document. This is shared on RPubs .
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Jupyter Notebook: An Introduction by Real Python: A brief introduction of Jupyter Notebook, including how to get started, elements in a Jupyter Notebook, how to explore a Notebook and Notebook extensions.
Jupyter Notebook Tutorial: The Definitive Guide by Data Camp: A tutorial introduces the history of Jupyter Notebook, and how to install, use and create a Notebook document. In addition, it offers tips, best practices and examples of Jupyter Notebook.
A gallery of interesting Jupyter Notebooks : A list of Jupyter Notebook examples in various disciplines on GitHub.
Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks : A journal article on PLoS Computational Biology. It provides 10 rules to best sharing data anlaysis in Jupyter Notebooks.
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A good readme template by PurpleBooth : A good ReadMe template for GitHub users.
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Computational physics ReadMe template : A ReadMe template on GitLab for researchers work in computational physics field, but could also be expanded to other fields.
Introduction of version control system, why it is important and best practices for version control. In addition, some resources about Git, GitHub and GitHub Desktop including installation instructions, documentation and tutorials.
Source code management : A tutorial created by BitBucket about the importance of source code management, and the benefits and best practices of it.
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About version control: An introduction to different version control systems on the Git's official site.
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GitHub Guides : Online guides for using some basic features of GitHub.
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In 2020, Pew Research Center launched a new project called the National Public Opinion Reference Survey (NPORS) . NPORS is an annual, cross-sectional survey of U.S. adults. Respondents can answer by paper, online or over the phone, and they are selected using address-based sampling from the United States Postal Service’s Computerized Delivery Sequence File. The response rate to the latest NPORS was 32%, and previous years’ surveys were designed with a similarly rigorous approach.
NPORS estimates are separate from the American Trends Panel (ATP) – the Center’s national online survey platform. Pew Research Center launched NPORS to address a limitation that researchers observed in the ATP. While the ATP was well-suited for the vast majority of the Center’s U.S. survey work, estimates for a few outcomes were not in line with other high-quality surveys, even after weighting to demographics like age, education, race and ethnicity, and gender.
For example, in 2018, roughly one-quarter of U.S. adults were religiously unaffiliated (i.e., atheist, agnostic or “nothing in particular”), according to the General Social Survey (GSS) and the Center’s own telephone-based polling . The ATP, however, estimated the religiously unaffiliated rate at about 32%. The Center did not feel comfortable publishing that ATP estimate because there was too much evidence that the rate was too high, likely because the types of people willing to participate in an online panel skew less religious than the population as a whole. Similarly, the ATP estimate for the share of U.S. adults identifying as a Democrat or leaning to the Democratic Party was somewhat higher than the rate indicated by the GSS and our own telephone surveys .
From 2014 to late 2020, the Center approached these outcomes slightly differently. We addressed the political partisanship issue by weighting every ATP survey to an external benchmark for the share of Americans identifying as a Republican, Democrat or independent. For the benchmark, we used the average of the results from our three most recent national cellphone and landline random-digit-dial (RDD) surveys.
During this time period, ATP surveys were not weighted to an external benchmark for Americans’ religious affiliation. The ATP was used for some research on religious beliefs and behaviors, but it was not used to estimate the overall share of Americans identifying as religiously affiliated or unaffiliated, nor was it used to estimate the size of particular faith groups, such as Catholics, Protestants or the Church of Jesus Christ of Latter-day Saints. NPORS allows us to improve and harmonize our approach to both these outcomes (Americans’ political and religious affiliations).
Read our fact sheet to find the latest NPORS estimates as well as methodological details. Data collection for NPORS was performed by Ipsos from 2020 through 2023 and is now performed by SSRS.
Several features of NPORS set it apart from a typical public opinion poll.
These features are not possible in most public polls for a host of reasons. But NPORS is designed to produce estimates of high enough quality that they can be used as weighting benchmarks for other polls, and so these features are critical.
The “R” in NPORS stands for “reference.” In this context, the term comes from studies in which researchers calibrate a small sample survey to a large, high-quality survey with greater precision and accuracy. Examples of reference surveys used by researchers include the Census Bureau’s American Community Survey (ACS) and the Current Population Survey (CPS). NPORS is not on the scale of the ACS or CPS, nor does it feature face-to-face data collection. But it does have something that those studies lack: timely estimates of key public opinion outcomes. Other studies like the American National Election Survey (ANES) and the General Social Survey collect key public opinion measures, but their data is released months, if not years, after data collection. The ANES, while invaluable to academic researchers, also excludes noncitizens who constitute about 7% of adults living in the U.S. and are included in the Center’s surveys.
NPORS is truly a reference survey for Pew Research Center because researchers weight each American Trends Panel wave to several NPORS estimates. In other words, ATP surveys refer to NPORS in order to represent groups like Republicans, Democrats, religiously affiliated adults and religiously unaffiliated adults proportional to their share of the U.S. population. The ATP weighting protocol also calibrates to other benchmarks, such as ACS demographic figures and CPS benchmarks for voter registration status and volunteerism.
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Pew Research Center started implementing this approach in 2021. It begins with measuring the survey panelists’ political party affiliation at a certain point in time (typically, each summer). Ideally, the reference survey will measure the same construct at the same point in time. We launched NPORS because we control its timing as well as the American Trends Panel’s timing, allowing us to achieve this syncing.
NPORS and ATP measurements of political party are collected at approximately the same time each summer. We may then conduct roughly 25 surveys on the ATP over the next year. For each of those 25 surveys, we append the panelists’ party affiliation answers from the summer to the current survey. To illustrate, let’s say that a survey was conducted in December. When researchers weight the December ATP survey, they take the measurement of party taken in the summer and weight that to the NPORS estimates for the partisan distribution of U.S. adults during the summer time frame. If, for example, Democrats were more likely than Republicans to respond to the December survey, the weighting to the NPORS target would help reduce the differential partisan nonresponse bias.
Critically, if the hypothetical December poll featured a fresh measurement of political party affiliation (typically asked about three times a year on the ATP), the new December answers do not get forced to any target. The new partisan distribution is allowed to vary. In this way, we can both address the threat from differential partisan nonresponse and measure an attitude that changes over time (without dictating the outcome). Each summer, the process starts anew by measuring political party on the ATP at basically the same time as the NPORS data collection.
A key feature of NPORS is that respondents are not members of a survey panel. It is a fresh, random sample of U.S. adults. This matters because some people are willing to take a onetime survey like NPORS but are not interested in taking surveys on an ongoing basis as part of a panel. That said, in certain years, NPORS serves as a recruitment survey for the ATP. After the NPORS questions, we ask respondents if they would be willing to take future surveys. People who accept and those who decline are both part of the NPORS survey. But only those who consent to future surveys are eventually invited to join the ATP.
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This paper combines personnel records of the U.S. federal government with census data to study how shocks to the gender composition of a large organization can persistently shift gender norms. Exploiting city-by-department variation in the sudden expansion of female clerical employment driven by World War I, we find that daughters of civil servants exposed to female co-workers are more likely to work later in life, command higher income, and have fewer children. These intergenerational effects increase with the size of the city-level exposure to female government workers and are driven by daughters in their teenage years at the time of exposure. We also show that cities exposed to a larger increase in female federal workers saw persistently higher female labor force participation in the public sector, as well as modest contemporaneous increases in private sector labor force participation suggestive of spill-overs. Collectively, the results are consistent with both the vertical and horizontal transmission of gender norms and highlight how increasing gender representation within the public sector can have broader labor market implications.
We thank seminar participants at the Chicago Economic History workshop, Duke, UBC, Simon Fraser, Nottingham, LSE, and Vanderbilt for their helpful suggestions. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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Title: scaling synthetic data creation with 1,000,000,000 personas.
Abstract: We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce Persona Hub -- a collection of 1 billion diverse personas automatically curated from web data. These 1 billion personas (~13% of the world's total population), acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, thereby facilitating the creation of diverse synthetic data at scale for various scenarios. By showcasing Persona Hub's use cases in synthesizing high-quality mathematical and logical reasoning problems, instructions (i.e., user prompts), knowledge-rich texts, game NPCs and tools (functions) at scale, we demonstrate persona-driven data synthesis is versatile, scalable, flexible, and easy to use, potentially driving a paradigm shift in synthetic data creation and applications in practice, which may have a profound impact on LLM research and development.
Comments: | Work in progress |
Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
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Author notification: Sep 25, 2024
Camera-ready, poster, and video submission: Oct 30, 2024 AOE
Submit at: https://openreview.net/group?id=NeurIPS.cc/2024/Conference
The site will start accepting submissions on Apr 22, 2024
Subscribe to these and other dates on the 2024 dates page .
The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024) is an interdisciplinary conference that brings together researchers in machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following:
Machine learning is a rapidly evolving field, and so we welcome interdisciplinary submissions that do not fit neatly into existing categories.
Authors are asked to confirm that their submissions accord with the NeurIPS code of conduct .
Formatting instructions: All submissions must be in PDF format, and in a single PDF file include, in this order:
Other supplementary materials such as data and code can be uploaded as a ZIP file
The main text of a submitted paper is limited to nine content pages , including all figures and tables. Additional pages containing references don’t count as content pages. If your submission is accepted, you will be allowed an additional content page for the camera-ready version.
The main text and references may be followed by technical appendices, for which there is no page limit.
The maximum file size for a full submission, which includes technical appendices, is 50MB.
Authors are encouraged to submit a separate ZIP file that contains further supplementary material like data or source code, when applicable.
You must format your submission using the NeurIPS 2024 LaTeX style file which includes a “preprint” option for non-anonymous preprints posted online. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review. Papers may be rejected without consideration of their merits if they fail to meet the submission requirements, as described in this document.
Paper checklist: In order to improve the rigor and transparency of research submitted to and published at NeurIPS, authors are required to complete a paper checklist . The paper checklist is intended to help authors reflect on a wide variety of issues relating to responsible machine learning research, including reproducibility, transparency, research ethics, and societal impact. The checklist forms part of the paper submission, but does not count towards the page limit.
Please join the NeurIPS 2024 Checklist Assistant Study that will provide you with free verification of your checklist performed by an LLM here . Please see details in our blog
Supplementary material: While all technical appendices should be included as part of the main paper submission PDF, authors may submit up to 100MB of supplementary material, such as data, or source code in a ZIP format. Supplementary material should be material created by the authors that directly supports the submission content. Like submissions, supplementary material must be anonymized. Looking at supplementary material is at the discretion of the reviewers.
We encourage authors to upload their code and data as part of their supplementary material in order to help reviewers assess the quality of the work. Check the policy as well as code submission guidelines and templates for further details.
Use of Large Language Models (LLMs): We welcome authors to use any tool that is suitable for preparing high-quality papers and research. However, we ask authors to keep in mind two important criteria. First, we expect papers to fully describe their methodology, and any tool that is important to that methodology, including the use of LLMs, should be described also. For example, authors should mention tools (including LLMs) that were used for data processing or filtering, visualization, facilitating or running experiments, and proving theorems. It may also be advisable to describe the use of LLMs in implementing the method (if this corresponds to an important, original, or non-standard component of the approach). Second, authors are responsible for the entire content of the paper, including all text and figures, so while authors are welcome to use any tool they wish for writing the paper, they must ensure that all text is correct and original.
Double-blind reviewing: All submissions must be anonymized and may not contain any identifying information that may violate the double-blind reviewing policy. This policy applies to any supplementary or linked material as well, including code. If you are including links to any external material, it is your responsibility to guarantee anonymous browsing. Please do not include acknowledgements at submission time. If you need to cite one of your own papers, you should do so with adequate anonymization to preserve double-blind reviewing. For instance, write “In the previous work of Smith et al. [1]…” rather than “In our previous work [1]...”). If you need to cite one of your own papers that is in submission to NeurIPS and not available as a non-anonymous preprint, then include a copy of the cited anonymized submission in the supplementary material and write “Anonymous et al. [1] concurrently show...”). Any papers found to be violating this policy will be rejected.
OpenReview: We are using OpenReview to manage submissions. The reviews and author responses will not be public initially (but may be made public later, see below). As in previous years, submissions under review will be visible only to their assigned program committee. We will not be soliciting comments from the general public during the reviewing process. Anyone who plans to submit a paper as an author or a co-author will need to create (or update) their OpenReview profile by the full paper submission deadline. Your OpenReview profile can be edited by logging in and clicking on your name in https://openreview.net/ . This takes you to a URL "https://openreview.net/profile?id=~[Firstname]_[Lastname][n]" where the last part is your profile name, e.g., ~Wei_Zhang1. The OpenReview profiles must be up to date, with all publications by the authors, and their current affiliations. The easiest way to import publications is through DBLP but it is not required, see FAQ . Submissions without updated OpenReview profiles will be desk rejected. The information entered in the profile is critical for ensuring that conflicts of interest and reviewer matching are handled properly. Because of the rapid growth of NeurIPS, we request that all authors help with reviewing papers, if asked to do so. We need everyone’s help in maintaining the high scientific quality of NeurIPS.
Please be aware that OpenReview has a moderation policy for newly created profiles: New profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.
Venue home page: https://openreview.net/group?id=NeurIPS.cc/2024/Conference
If you have any questions, please refer to the FAQ: https://openreview.net/faq
Abstract Submission: There is a mandatory abstract submission deadline on May 15, 2024, six days before full paper submissions are due. While it will be possible to edit the title and abstract until the full paper submission deadline, submissions with “placeholder” abstracts that are rewritten for the full submission risk being removed without consideration. This includes titles and abstracts that either provide little or no semantic information (e.g., "We provide a new semi-supervised learning method.") or describe a substantively different claimed contribution. The author list cannot be changed after the abstract deadline. After that, authors may be reordered, but any additions or removals must be justified in writing and approved on a case-by-case basis by the program chairs only in exceptional circumstances.
Ethics review: Reviewers and ACs may flag submissions for ethics review . Flagged submissions will be sent to an ethics review committee for comments. Comments from ethics reviewers will be considered by the primary reviewers and AC as part of their deliberation. They will also be visible to authors, who will have an opportunity to respond. Ethics reviewers do not have the authority to reject papers, but in extreme cases papers may be rejected by the program chairs on ethical grounds, regardless of scientific quality or contribution.
Preprints: The existence of non-anonymous preprints (on arXiv or other online repositories, personal websites, social media) will not result in rejection. If you choose to use the NeurIPS style for the preprint version, you must use the “preprint” option rather than the “final” option. Reviewers will be instructed not to actively look for such preprints, but encountering them will not constitute a conflict of interest. Authors may submit anonymized work to NeurIPS that is already available as a preprint (e.g., on arXiv) without citing it. Note that public versions of the submission should not say "Under review at NeurIPS" or similar.
Dual submissions: Submissions that are substantially similar to papers that the authors have previously published or submitted in parallel to other peer-reviewed venues with proceedings or journals may not be submitted to NeurIPS. Papers previously presented at workshops are permitted, so long as they did not appear in a conference proceedings (e.g., CVPRW proceedings), a journal or a book. NeurIPS coordinates with other conferences to identify dual submissions. The NeurIPS policy on dual submissions applies for the entire duration of the reviewing process. Slicing contributions too thinly is discouraged. The reviewing process will treat any other submission by an overlapping set of authors as prior work. If publishing one would render the other too incremental, both may be rejected.
Anti-collusion: NeurIPS does not tolerate any collusion whereby authors secretly cooperate with reviewers, ACs or SACs to obtain favorable reviews.
Author responses: Authors will have one week to view and respond to initial reviews. Author responses may not contain any identifying information that may violate the double-blind reviewing policy. Authors may not submit revisions of their paper or supplemental material, but may post their responses as a discussion in OpenReview. This is to reduce the burden on authors to have to revise their paper in a rush during the short rebuttal period.
After the initial response period, authors will be able to respond to any further reviewer/AC questions and comments by posting on the submission’s forum page. The program chairs reserve the right to solicit additional reviews after the initial author response period. These reviews will become visible to the authors as they are added to OpenReview, and authors will have a chance to respond to them.
After the notification deadline, accepted and opted-in rejected papers will be made public and open for non-anonymous public commenting. Their anonymous reviews, meta-reviews, author responses and reviewer responses will also be made public. Authors of rejected papers will have two weeks after the notification deadline to opt in to make their deanonymized rejected papers public in OpenReview. These papers are not counted as NeurIPS publications and will be shown as rejected in OpenReview.
Publication of accepted submissions: Reviews, meta-reviews, and any discussion with the authors will be made public for accepted papers (but reviewer, area chair, and senior area chair identities will remain anonymous). Camera-ready papers will be due in advance of the conference. All camera-ready papers must include a funding disclosure . We strongly encourage accompanying code and data to be submitted with accepted papers when appropriate, as per the code submission policy . Authors will be allowed to make minor changes for a short period of time after the conference.
Contemporaneous Work: For the purpose of the reviewing process, papers that appeared online within two months of a submission will generally be considered "contemporaneous" in the sense that the submission will not be rejected on the basis of the comparison to contemporaneous work. Authors are still expected to cite and discuss contemporaneous work and perform empirical comparisons to the degree feasible. Any paper that influenced the submission is considered prior work and must be cited and discussed as such. Submissions that are very similar to contemporaneous work will undergo additional scrutiny to prevent cases of plagiarism and missing credit to prior work.
Plagiarism is prohibited by the NeurIPS Code of Conduct .
Other Tracks: Similarly to earlier years, we will host multiple tracks, such as datasets, competitions, tutorials as well as workshops, in addition to the main track for which this call for papers is intended. See the conference homepage for updates and calls for participation in these tracks.
Experiments: As in past years, the program chairs will be measuring the quality and effectiveness of the review process via randomized controlled experiments. All experiments are independently reviewed and approved by an Institutional Review Board (IRB).
Financial Aid: Each paper may designate up to one (1) NeurIPS.cc account email address of a corresponding student author who confirms that they would need the support to attend the conference, and agrees to volunteer if they get selected. To be considered for Financial the student will also need to fill out the Financial Aid application when it becomes available.
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An important tip: Naming files should be descriptive and consistent! Date format (ISO 8601 Standard): YYYYMMDDThhmmss. Make file names sortable and searchable: Project or experiment name. Researcher name/initials. Date or date range of collection version. An example for README file. An example of code documentation.
Set the top, bottom, and side margins of your paper at 1 inch. Use double-spaced text throughout your paper. Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point). Use continuous pagination throughout the paper, including the title page and the references section.
Definition: Research Paper is a written document that presents the author's original research, analysis, and interpretation of a specific topic or issue. It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new ...
In fact, proper documentation is just a matter of establishing the who, when, what, and where of the sources as applicable. APA Style documents sources in the body of the paper with in-text citations and at the end on a list of references. In-text citations need to provide the who, when, and where for each work; reference entries will include ...
22.1 Choosing a Documentation Format. As a rule, your assignments requiring research will specify a documentation format. If you are free to use the style of your choice, you can choose any format you want as long as you are consistent, but you should know that certain disciplines tend to use specific documentation styles:
Table of contents. Step 1: Introduce your topic. Step 2: Describe the background. Step 3: Establish your research problem. Step 4: Specify your objective (s) Step 5: Map out your paper. Research paper introduction examples. Frequently asked questions about the research paper introduction.
A renewed emphasis on teaching research documentation practices is necessary at this point because recent technological advances have made research documentation both more convenient and more complicated. Decades ago, everything researchers did was on paper, and document-ing research largely meant storing a lot of file boxes and folders. Clunky
Updated on November 04, 2019. In a report or research paper, documentation is the evidence provided for information and ideas borrowed from others. That evidence includes both primary sources and secondary sources . There are numerous documentation styles and formats, including MLA style (used for research in the humanities), APA style ...
The two most common types of documentation used in research are note citations and parenthetical citations (Winkler & McCuen-Metherell, 2008, p. 4). You might also see terms like "footnotes," "endnotes," or "references" when learning about documentation practices. Refer to the required style guide and your instructor when ...
APA also includes rules for organization of a paper including title page, abstract, main body (introduction, method, results, discussion), and references. Appendices, if any, follow the references. Papers which report the results of studies or experiments typically use this organization. Typically, students are asked to do a research paper ...
THE DOCUMENTED ESSAYAPA Documentation StyleWhen writing a research paper, you must document everything from an outside source that you incorporate within your text, including direct quotation, your summa. y of ideas, and any paraphrased information. You must indicate the source of any appropriated material that.
Apply guidelines for citing sources within the body of the paper and the bibliography. Use primary and secondary research to support ideas. Identify the purposes for which writers use each type of research. At last, you are ready to begin writing the rough draft of your research paper. Putting your thinking and research into words is exciting.
A documentation style is a standard approach to the citation of sources that the author of a paper has consulted, abstracted, or quoted from. It prescribes methods for citing references within the text, providing a list of works cited at the end of the paper, and even formatting headings and margins.
In this chapter, you will learn how to use APA style, the documentation and formatting style followed by the American Psychological Association, as well as MLA style, from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian: ... A college research paper may not ...
Research paper format is an essential aspect of academic writing that plays a crucial role in the communication of research findings.The format of a research paper depends on various factors such as the discipline, style guide, and purpose of the research. It includes guidelines for the structure, citation style, referencing, and other elements of the paper that contribute to its overall ...
Research documentation should include all the information that is needed to understand the underlying design for the research output. This can include descriptions of: ... If you are preparing documentation to accompany the publication of an academic output such as a working paper or journal article, the most common form of research ...
THE DOCUMENTED ESSAYGeneral GuidelinesA research paper or documented essay is a piece of writing in which you incorporate information—facts, arguments, opinions—taken from the writin. s of authorities in a particular field. Sometimes a research paper is no more than a report of current thinking in a field, but more often a research paper ...
Inadequacies in documentation could be the result of lack of training and experience in good understanding of clinical research and documentation requirements. ... verifying eligibility, use of right tools such as diaries, source document worksheets, OPD papers, copies of prescriptions, etc; ways to avoid multiple records and in case of ...
To date, research on this topic has mainly focused on the difference between paper-based and electronic documentation [11-13]. Although reuse of data, for which structured documentation is essential, will become increasingly important, the primary goal of EHR documentation is supporting high-quality patient care . Therefore, the primary ...
This paper aims to act as a cross-disciplinary reference for the analysis of documents. across different specialised disciplines. Given the rising quality, quantity, accessibility, and ...
Best Practices for Code Documentation. General guidelines and best practices for documenting code: A Guide to Reproducible Code in Ecology and Evolution : A guide to write reproducible code for researchers in Ecology and Evolution fields, created by British Ecological Society . Most guidelines can still be applied to other disciplines.
documentation globally, with greater emphasis on scientific documents. Moreover, it will open a new avenue for further discussion under the heading of problematic documentation. This paper aims to act as a cross-disciplinary reference for the analysis of documents across different specialised disciplines.
Tools for writing a research paper introduction. Now that we've introduced you to the basics of writing a research paper introduction, we'd like to introduce you to QuillBot. At every step of writing your intro, it can help you upgrade your writing skills: Cite sources using the Citation Generator. Avoid plagiarism using the Plagiarism Checker.
Traditional paper documentation is plagued by several issues such as incomplete data records, missing nurse signatures in many nursing notes, and illegible handwriting and abbreviations, leading ...
Grade 1: 11 μm (medium flow filter paper) The most widely used filter paper for routine applications with medium retention and flow rate. Extended range of sizes includes 10 to 500 mm diameter circles and 460 x 570 mm sheets. This grade covers a wide range of laboratory applications and is frequently used for clarifying liquids.
In 2020, Pew Research Center launched a new project called the National Public Opinion Reference Survey (NPORS). NPORS is an annual, cross-sectional survey of U.S. adults. Respondents can answer either by paper or online, and they are selected using address-based sampling from the United States Postal Service's computerized delivery sequence file.
This paper combines personnel records of the U.S. federal government with census data to study how shocks to the gender composition of a large organization can persistently shift gender norms. Exploiting city-by-department variation in the sudden expansion of female clerical employment driven by ...
This review problematizes current research on social entrepreneurial crowdfunding based on its underlying assumptions and potential blind spots. By introducing the Communicative Constitution of Organizations (CCO) theory as an alternative approach, directions for prospective research are suggested. Specifically, based on a critical analysis of the research literature on social entrepreneurial ...
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce Persona Hub -- a collection of 1 billion diverse personas automatically curated from web data. These 1 billion personas (~13% of the world's total population), acting as ...
Paper checklist: In order to improve the rigor and transparency of research submitted to and published at NeurIPS, authors are required to complete a paper checklist. The paper checklist is intended to help authors reflect on a wide variety of issues relating to responsible machine learning research, including reproducibility, transparency ...