The Assessment Burden in Competency-Based Medical Education: How Programs Are Adapting
Bibliographic record
Abstract
Residents and faculty have described a burden of assessment related to the implementation of competency-based medical education (CBME), which may undermine its benefits. Although this concerning signal has been identified, little has been done to identify adaptations to address this problem. Grounded in an analysis of an early Canadian pan-institutional CBME adopter's experience, this article describes postgraduate programs' adaptations related to the challenges of assessment in CBME. From June 2019-September 2022, 8 residency programs underwent a standardized Rapid Evaluation guided by the Core Components Framework (CCF). Sixty interviews and 18 focus groups were held with invested partners. Transcripts were analyzed abductively using CCF, and ideal implementation was compared with enacted implementation. These findings were then shared back with program leaders, adaptations were subsequently developed, and technical reports were generated for each program. Researchers reviewed the technical reports to identify themes related to the burden of assessment with a subsequent focus on identifying adaptations across programs. Three themes were identified: (1) disparate mental models of assessment processes in CBME, (2) challenges in workplace-based assessment processes, and (3) challenges in performance review and decision making. Theme 1 included entrustment interpretation and lack of shared mindset for performance standards. Adaptations included revising entrustment scales, faculty development, and formalizing resident membership. Theme 2 involved direct observation, timeliness of assessment completion, and feedback quality. Adaptations included alternative assessment strategies beyond entrustable professional activity forms and proactive assessment planning. Theme 3 related to resident data monitoring and competence committee decision making. Adaptations included adding resident representatives to the competence committee and assessment platform enhancements. These adaptations represent responses to the concerning signal of significant burden of assessment within CBME being experienced broadly. The authors hope other programs may learn from their institution's experience and navigate the CBME-related assessment burden their invested partners may be facing.
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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 | 0.052 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".