What Factors are Important to the Success of Resubmitted Grant Applications in Health Research? A Retrospective Study of Over 20,000 Applications to the Canadian Institutes of Health Research
Bibliographic record
Abstract
Abstract Background In this retrospective study, we investigated the outcomes (funded/not funded) and factors related to the funding of resubmitted applications to the Canadian Institutes of Health Research (CIHR) Open Operating Grants Competition and Project Grant Competition between 2000 and 2022. Method and Findings The primary outcome was the proportion of resubmissions and new applications that were funded. Using a random forest model, we explored the importance of variables related to the success of resubmissions. A higher proportion of resubmissions (∼23%) were funded compared to new submissions (∼12%). The most important variables related to resubmission success were the rank (%) and score (/5) given to the preceding (initial) application and the number of the number of CIHR-funded grants where the PI was a named team member. Conclusion Resubmitting applications to the CIHR Project Grant Competition was beneficial, particularly for projects that were previously highly ranked and received high scores. These results may offer guidance for researchers who are deciding whether to resubmit rejected applications.
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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 | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".