Exploring residents’ perceptions of competency-based medical education across Canada: A national survey study
Bibliographic record
Abstract
Background: As Competency-Based Medical Education (CBME) is implemented across Canada, little is known about residents’ perceptions of this model. This study examined how Canadian residents understand CBME and their lived experiences with implementation. Methods: We administered a survey in 2018 with Likert-type and open-ended questions to 375 residents across Canada, of whom 270 were from traditional programs (“pre-CBME”) and 105 were in a CBME program. We used the Mann-Whitney test to examine differences across samples, and analyzed qualitative data thematically. Results: Three themes were identified across both groups: program outcome concerns, changes, and emotional responses. In relation to program concerns, both groups were concerned about the administrative burden, challenges with the assessment process, and feedback quality. Only pre-CBME residents were concerned about faculty engagement and buy-in. In terms of changes, both groups discussed a more formalized assessment process with mixed reactions. Residents in the pre-CBME sample reported greater concerns for faculty time constraints, assessment completion, and quality of learning experiences, whilst those in CBME programs reported being more proactive in their learning and greater self-reflection. Residents expressed strong emotional narrative responses including greater stress and frustration in a CBME environment. Conclusion: Findings demonstrate that residents have mixed feelings and experiences regarding CBME. Their positive experiences align with the aim of developing more self-directed learners. However, the concerns suggest the need to address specific shortcomings to increase buy-in, while the emotional responses associated with CBME may require a cultural shift within residency programs to guard against burnout.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".