Internal medicine residents’ perceptions of the Mini-Clinical Evaluation Exercise
Bibliographic record
Abstract
BACKGROUND: The mini-clinical evaluation exercise (mini-CEX) is a 30 minute observed clinical encounter which allows assessment of a resident's clinical competence with feedback on their performance. AIMS: To assess residents' perceptions of the mini-CEX using qualitative methods. METHODS: After introducing the mini-CEX into the University of British Columbia's Internal Medicine Residency Program, a one hour semi-structured focus group with voluntary first and second year residents was undertaken. The focus groups were conducted by an independent moderator, audio-taped, and transcribed verbatim. Using a phenomenological approach, the comments made by the focus group participants were read independently by the three authors and organized into major themes. RESULTS: The major themes included Education, Assessment and Exam Preparation. Residents described a conflict between the mini-CEX's role as a method of assessment and its utility as an educational tool. During initial mini-CEX encounters, they perceived the assessment format as anxiety-provoking. Over time, they felt that the mini-CEX provided insight into their clinical competence. Participants believed that the mini-CEX experience would benefit them in preparation and successful completion of their national specialty exam. CONCLUSIONS: Residents' perceptions of the mini-CEX reflected a tension between the tool's dual roles of assessment and education.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".