The impact of local health professions education grants: is it worth the investment?
Bibliographic record
Abstract
Background: Local grants programs are important since funding for medical education research is limited. Understanding which factors predict successful outcomes is highly relevant to administrators. The purpose of this project was to identify factors that contribute to the publication of local medical education grants in a Canadian context. Methods: Surveys were distributed to previous Department of Innovation in Medical Education (DIME) and Department of Medicine (DOM) grant recipients (n = 115) to gather information pertaining to PI demographics and research outcomes. A backward logistic regression was used to determine the effects several variables on publication success. Results: The overall publication rate was 64/115 (56%). Due to missing data, 91 grants were included in the logistic regression. Variables associated with a higher rate of publication; cross departmental compared to single department OR = 2.82 (p = 0.04), being presented OR = 3.30 (p = 0.01), and multiple grant acquisition OR = 3.85 (p = 0.005) Conclusion: Although preliminary, our data suggest that increasing research publications from local grants may be facilitated by pooling funds across departments, making research presentations mandatory, and allowing successful researchers to re-apply.
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.
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 | 0.056 | 0.281 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".