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National Institutes of Health Funding Gaps for Principal Investigators

2023· article· en· W4386860893 on OpenAlexaboutno aff
Kelly M. Gillen, Daniel Markowitz, Patricia J. Long, Adriel Villegas‐Estrada, Eileen Wang Chang, Ajay Gupta

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
FundersNational Institutes of HealthNational Institute for Health and Care Research
KeywordsQuarter (Canadian coin)Fiscal yearPrincipal (computer security)Political scienceMedicineBusinessGeographyFinance

Abstract

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Importance: Early-stage and established investigators compete for a limited supply of funds from the National Institutes of Health (NIH). Regardless of their previous funding success, many principal investigators (PIs) encounter a funding gap in which they no longer receive ongoing funding from the NIH. Objective: To determine incidence rates of PI-level funding gaps, the mean funding gap length, and whether these 2 metrics are associated with previous funding success. Design, Setting, and Participants: This study was conducted using data from NIH RePORTER. Historical datafiles for fiscal year (FY) 2011 to FY 2021 were aggregated to generate 2 master datafiles for this period: all NIH awards and only R01 awards. PIs with no funding in FY 2011 or FY 2021 were removed. PIs were sorted by FY 2011 total funding amounts and grouped by quarter of amount. Results: A total of 39 944 unique researchers were awarded 220 131 NIH awards, of which 103 753 were R01 awards. For all NIH awards, there was an overall linear increase from top quarter to bottom quarter in the percentage of PIs who had at least 1 year without funding (from 27% to 75%), percentage of these gap PIs who had at least 2 consecutive years without funding (from 56% to 68%), and mean maximum consecutive years without funding for gap PIs (2.2 years to 3.1 years). For only R01 awards, there was an overall linear increase from top quarter to bottom quarter in the percentage of PIs who had at least 1 year without funding (50% to 74%), percentage of gap PIs who had at least 2 consecutive years without funding (59% to 71%), and mean maximum consecutive years without funding for gap PIs (2.4 years to 3.1 years). Conclusions and Relevance: In this cohort study of NIH-funded investigators, PIs with higher NIH funding were less likely to experience a funding gap. Additionally, when these PIs encountered a funding gap, this period without funding was shorter; however, among all PIs, funding gaps typically lasted 2 to 3 years. These associations were found inclusive of all NIH awards and when analysis was limited to only R01 awards. These findings may be useful to PIs and academic institutions as they prepare, structure, and project research resource allocations.

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How this classification was reachedexpand

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)0.009
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.404
GPT teacher head0.509
Teacher spread0.106 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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