Rapid, collaborative generation and review of COVID-19 pandemic-specific competencies for family medicine residency training
Bibliographic record
Abstract
BACKGROUND: In March 2020, the COVID-19 pandemic disrupted competency-based medical education in Family Medicine programs across Canada. Faculty and residents identified a need for clear, relevant, and specific competencies to frame teaching, learning, supervision and feedback during the pandemic. METHODS: A rapid, iterative, educational quality improvement process was launched. Phase 1 involved experienced educators defining gaps in our program's existing competency-database, reviewing emerging public health and regulatory guidelines, and drafting competencies. Phase 2 involved translation, member-checking, and anonymous feedback and editing of draft competencies by residents and other educational leaders. Phase 3 involved wider dissemination, collaborative editing and feedback from residents and faculty throughout the department. RESULTS: A total of 44 physicians including residents and faculty from multiple contexts provided detailed feedback, review, and editing of an ultimate list of 33 competencies organized by CanMEDS-FM roles. Broad agreement was obtained that the competencies form reasonable learning outcomes during the COVID-19 pandemic. CONCLUSIONS: These competencies represent learning objectives reflecting the initial educational mindsets of a wide range of teachers and learners experiencing a global pandemic. The project illustrates a novel collaboration across educational portfolios as a rapid educational response to a public health crisis.
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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 | 0.123 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".