Building capacity for medical education research in family medicine: the Program for Innovation in Medical Education (PIME)
Bibliographic record
Abstract
BACKGROUND: Despite the apparent benefits to teaching, many faculty members are reluctant to participate in medical education research (MER) for a variety of reasons. In addition to the further demand on their time, physicians often lack the confidence to initiate MER projects and require more support in the form of funding, structure and guidance. These obstacles have contributed to a decline in physician participation in MER as well as to a perceived decay in its quality. As a countermeasure to encourage physicians to undertake research, the Department of Family Medicine at the University of Ottawa implemented a programme in which physicians receive the funding, coaching and support staff necessary to complete a 2-year research project. The programme is intended primarily for first-time researchers and is meant to serve as a gateway to a research career funded by external grants. Since its inception in 2010, the Program for Innovation in Medical Education (PIME) has supported 16 new clinician investigators across 14 projects. METHODS: We performed a programme evaluation 3 years after the programme launched to assess its utility to participants. This evaluation employed semi-structured interviews with physicians who performed a research project within the programme. RESULTS: Programme participants stated that their confidence in conducting research had improved and that they felt well supported throughout their project. They appreciated the collaborative nature of the programme and remarked that it had improved their willingness to solicit the expertise of others. Finally, the programme allowed participants to develop in the scholarly role expected by family physicians in Canada. CONCLUSION: The PIME may serve as a helpful model for institutions seeking to engage faculty physicians in Medical Education Research and to thereby enhance the teaching received by their medical learners.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".