Program directors’ reflections on national policy change in medical education: insights on decision-making, accreditation, and the CanMEDS framework
Bibliographic record
Abstract
BACKGROUND: Outcomes of national policy change impact all levels of the organizational hierarchy. The medical education literature is sparse on how reflections from program directors (PDs) on past large-scale policy changes can inform future policy initiatives. To fill this gap, we conducted a national survey on PDs' perceptions of, and reflections on, decision-making in medical education, accreditation procedures, and the CanMEDS framework implementation. METHODS: = 684). Descriptive analysis was performed on quantitative data, thematic analysis was performed on qualitative comments, and comparisons between the quantitative and qualitative findings were performed to identify areas of convergence and/or divergence. RESULTS: A total of 265 (38.7%) former PDs participated. Quantitative analysis revealed that 52.8% of respondents did not feel involved in decision-making regarding policy changes, 45.1% of respondents did not feel prepared to assess the CanMEDS Roles, and PDs were divided on the reasonableness of accreditation documentation. Qualitative analysis produced four themes: communication, resources, expectations of outcomes, and buy-in. Nine sub-themes were also identified. A high level of convergence was identified across the content, with only four areas of divergence identified. CONCLUSIONS: Our findings have the potential to inform future policy and/or accreditation changes. Without the lens of those charged with overseeing the implementation, policy evaluation and quality improvement will remain uninformed. PDs, therefore, bring unique insights into our understanding of national policy changes, and without the voices of these frontline implementers, the true success of policy change implementation will be hindered.
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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 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".