Bibliographic record
Abstract
Competency-based medical education (CBME) aims to modernize postgraduate training through developmental, learner--centered assessment. However, many residents still experience the process as procedural and detached from meaningful growth. Using self-determination theory, the authors examine how current CBME practices often undermine residents' needs for autonomy, competence, and relatedness, producing superficial compliance rather than internalization and authentic commitment. Beyond structural critique, they highlight agentic engagement-residents' proactive efforts to "pull" autonomy support and shape feedback-as an underused but essential lever for revitalizing CBME. Field notes and entrustable professional activities can serve as coaching tools rather than bureaucratic artifacts but only if situated within autonomy-supportive dialogue, trusting relationships, and competence-oriented feedback. Drawing from self-determination theory research, the authors outline evidence-based, need-supportive strategies for embedding CBME practices into routine workflows. Collectively, the recommendations offer educators a pragmatic guide for aligning assessment culture with resident motivation, professional identity formation, and well-being. Without motivational alignment, CBME risks remaining an exercise in form over substance.
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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 | 0.027 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| 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".