An output evaluation of a health research foundation’s enhanced grant review process for new investigators
Bibliographic record
Abstract
BACKGROUND: We assessed the ability of the Manitoba Medical Service Foundation (MMSF, a small not-for-profit foundation affiliated with Manitoba Blue Cross) to determine the best candidates for selection to receive research funding support among new researchers applying to the Research Operating Grants Programme (ROGP). METHODS: Using bibliometric and grants funding analyses, we retrospectively compared indices of academic outputs from five cohorts of MMSF-funded and not MMSF-funded applicants to the annual MMSF ROGP over 2008 to 2012, from 1 to 5 years after having received evaluation decisions from the MMSF enhanced grant review process. RESULTS: Those researchers funded by the MMSF competition (MMSF-funded) had a statistically significant greater number of publications, a higher h-index and greater national Tri-Council (TC) funding, versus those not selected for funding (not MMSF-funded). MMSF-funded applicants and the Manitoba research community have created a strong and rapid (within 1 to 5 years of receiving the MMSF grant) local economic return on investment associated with the MMSF ROGP that supports new investigators, of approximately nine-fold for TC grants by the principal investigator, and of 34-fold for the principal investigator on collaborative (total) TC grants. CONCLUSIONS: The use of small amounts of seed money for competitive research grants at early stages of an MMSF-funded applicant's career correlates with future short-term success of that applicant. The ability to correctly select promising candidates who subsequently demonstrate greater academic performance after the MMSF funding shows the selection process and the ROGP to be of merit. Multiple components may have contributed to this outcome, including a direct presentation and interview process of the candidate with five-person selection subcommittees, plus an assessment by an external reviewer (the enhanced grant review process). The selection methods used here may add value to the research grant selection processes of new researchers.
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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 | 0.373 | 0.550 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".