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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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 teacher head, 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".