Biomedical research grant resubmission: rates and factors related to success – a scoping review
Bibliographic record
Abstract
OBJECTIVES: Most first-time biomedical research grant applications are not funded. In the challenging research funding climate, resubmitting a grant application is a necessary task for scientists. Identifying which factors influence their decision to resubmit and the success of resubmissions will inform funders and applicants. However, data on resubmissions are fragmented and under-reported. In this scoping review, we aimed to summarise (1) the outcomes of resubmitting biomedical research grant applications and (2) the demographic characteristics of scientists who resubmitted grant applications. DESIGN: Scoping review with reporting informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. DATA SOURCES: MEDLINE, CINAHL, EMBASE, Cochrane Central Registrar of Controlled Trials CENTRAL, PsycINFO, Web of Science and grey literature sources were searched through November 2022. ELIGIBILITY CRITERIA: We included peer-reviewed and grey literature records from the biomedical sciences that reported outcomes of the resubmission process (eg, resubmission success rate, rate of resubmission) and information about the scientists who resubmit grant applications (eg, sex, race, career stage). DATA EXTRACTION AND SYNTHESIS: Data were extracted independently by two reviewers. The data were cross-referenced and any conflicts were resolved via consensus. Data were summarised descriptively and presented in tables and figures. RESULTS: Resubmissions represented a substantial proportion of applications (lowest prevalence rate: 4%; highest prevalence rate: 56%) in a given funding cycle and were reliably more successful than first-time applications (lowest success rate: 16%; highest success rate: 82%)-a phenomenon associated with several sociodemographic, institutional and project-related factors. There was conflicting evidence about the relationship of sociodemographic-related, institution-related and project-related factors to resubmission likelihood and success. CONCLUSION: The resubmission process is a time-consuming and often frustrating experience for researchers. Our review identified opportunities to streamline and improve the process to enhance the biomedical research landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.130 | 0.448 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.034 | 0.044 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".