Implementation of a clinician and academic researcher-led funding program to stimulate research in a Regional Medical Campus
Bibliographic record
Abstract
INTRODUCTION: Fostering locally initiated clinical research, with physicians as lead investigators, can be challenging for a Regional Medical Campus (RMC) or any site involved in distributed medical education (DME). Exposing students to research and to clinically relevant research is an important accreditation criterion. We discuss an initiative implemented to stimulate the development of clinical research activities within the main hospital affiliated with our RMC. METHODS: The Duo research grant program was launched in March 2018. It offers research grants worth up to 25,000 CAN$. Proposals have to be submitted by two co-principal investigators, including one academic researcher and one clinician involved in medical education through our RMC. Projects need to address a clinical practice or medical education issue.RESULTS: Twelve projects were submitted in the first two funding rounds of the Duo research grant program. Eight of the twelve proposals received funding (67% success rate) and have already directly exposed medical students and residents to clinical research. They have also led to presentations at conferences and submission of external grant proposals. CONCLUSIONS: With a cost of 100,000 CAN$ per year, the Duo research grant program appears to be an effective strategy for fostering meaningful collaborations between clinicians and researchers, for exposing our medical students to more clinical research, and for favouring the development of our clinicians’ academic profiles.
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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 | 0.081 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".