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Record W3159605412 · doi:10.7759/cureus.14644

Gender Disparity in Grants and Awards at the National Institute of Health

2021· article· en· W3159605412 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCureus · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsVancouver General Hospital
FundersNational Institutes of Health
KeywordsMedicineCareer developmentMedical educationPortfolioGrant fundingLibrary sciencePolitical sciencePublic administration

Abstract

fetched live from OpenAlex

Objective The National Institute of Health (NIH) supports the academic career of scientists across the United States (U.S.). It promotes and sponsors scientists in conducting wide-ranging clinical and basic science research. Depending on the duration, research type, and budget, there are various types of grants awarded by NIH. Despite considerable advancement in biomedical sciences, female researchers remain underrepresented in obtaining NIH funding. Through this study, we aim to highlight the gender trends in NIH funding and grants. By doing this, we aim to facilitate effective future policymaking to help achieve gender parity in NIH grants and awards. Methods The data were obtained from the NIH Research Portfolio Online Reporting Tool (RePORT). The extracted data by gender were tabulated showing percentages of females as Research Grant Investigators, Research Career Development Award Recipients and Kirschstein-National Research Service Award (NRSA) Trainees and Fellows, recipients of Research Grants, Research Project Grants (RPGs), and R01 equivalent grants including types 1 or 2, over two decades (1999-2019). Absolute percentage change was also calculated and included in the tables. Results The percentage of females as NIH Research Grant Investigators has increased at centers, research centers as well as for RPGs and Small Business Innovation Research and Small Business Technology Transfer (SBIR/STTR) programs. For Research Career Development Award Recipients and Kirschstein-NRSA Trainees and Fellows, the proportion of female pre-doctoral institutional trainees, post-doctoral fellows, post-doctoral institutional trainees, mentored research career awardees, and other research career awardees have steadily increased. However, there was a decrease in the percentage of female pre-doctoral fellow awardees. The percentage of females receiving all RPGs, R01-New (type 1) and R01-Renewal (type 2) grants has also decreased. Conclusion Despite an overall increase in the percentage of female researchers successfully receiving NIH grants and awards, they continue to lag compared to their male counterparts. With the increasing number of female doctoral graduates, it is imperative to address this disparity in NIH funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.105
GPT teacher head0.366
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it