Lost in translation? How context shapes the implementation of Competence by Design in operative settings
Bibliographic record
Abstract
BACKGROUND: Given the complexity of the transition to competency-based medical education (CBME) and the diversity of systems and learning contexts, the literature has acknowledged the need for principled yet contextual approaches to implementation. There is a need for research that examines these adaptations and their consequences, both intended and unintended. METHODS: We performed a constructivist grounded theory study to explore how the theory of CBME translated to practice in operative settings in a Canadian approach to CBME: Competence by Design (CBD). RESULTS: Program contexts both enabled and hindered how CBD translated into practice. The operative context was aligned with the principles of competency-focused instruction and allowed for frequent, direct observation and formative feedback. Time, personnel, and technology constraints unique to the patterns of practice in operative settings hindered programmatic assessment. CONCLUSION: Adaptations to CBME that are responsive to the context of programs can support the intended conceptual learning conditions of CBME.
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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 | 0.072 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".