Evaluation of a National Competency-Based Assessment System in Emergency Medicine: A CanDREAM Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Background In 2018, Canadian postgraduate emergency medicine (EM) programs began implementing a competency-based medical education (CBME) assessment program. Studies evaluating these programs have focused on broad outcomes using data from national bodies and lack data to support program-specific improvement. Objective We evaluated the implementation of a CBME assessment program within and across programs to identify successes and opportunities for improvement at the local and national levels. Methods Program-level data from the 2018 resident cohort were amalgamated and analyzed. The number of entrustable professional activity (EPA) assessments (overall and for each EPA) and the timing of resident promotion through program stages were compared between programs and to the guidelines provided by the national EM specialty committee. Total EPA observations from each program were correlated with the number of EM and pediatric EM rotations. Results Data from 15 of 17 (88%) programs containing 9842 EPA observations from 68 of 77 (88%) EM residents in the 2018 cohort were analyzed. Average numbers of EPAs observed per resident in each program varied from 92.5 to 229.6, correlating with the number of blocks spent on EM and pediatric EM (r = 0.83, P < .001). Relative to the specialty committee's guidelines, residents were promoted later than expected (eg, one-third of residents had a 2-month delay to promotion from the first to second stage) and with fewer EPA observations than suggested. Conclusions There was demonstrable variation in EPA-based assessment numbers and promotion timelines between programs and with national guidelines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it