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Record W3161293839 · doi:10.7759/cureus.14930

Gender Differences in Research Project Grants and R01 Grants at the National Institutes of Health

2021· article· en· W3161293839 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
KeywordsMedicineLibrary scienceFamily medicineGerontologyMedical educationDemographySociology

Abstract

fetched live from OpenAlex

Objectives The National Institutes of Health (NIH), which is the world's largest funding source for research, offers various types of competitive grants depending on the duration, research type, and budget. The Research Project Grant (RPG) is the oldest mechanism for grant allocation that is used by the NIH. In this study, we explored the gender trends of NIH RPGs and R01 grants over the last two decades. Methods By utilizing the NIH Research Portfolio Online Reporting Tool (RePORT), data for gender were extracted, and the percentage of women as RPGs Investigators, R01-equivalent grant including R01 type 1 and type 2 grant awardees, from 1998 to 2019 were tabulated. The absolute change was calculated. Results From 1998 to 2019, the percentage of female RPG awardees has increased. However, the success rates for female RPG applicants have decreased during the same period. The funding and success rates for new R01 awards have been similar for both men and women, but women have been less successful at the renewal of R01-equivalent awards. Conclusion Gender disparity exists in awardees of higher RPGs, including the R01 award. This highlights the need for further actions to ensure gender parity in grant allocations at the NIH.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.041
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.535
GPT teacher head0.489
Teacher spread0.046 · 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