Bibliographic record
Abstract
PROBLEM: To assess resident competence, generalist programs such as emergency medicine (EM), which cover a broad content and skills base, require a substantial number of work-based assessments (WBAs) that integrate qualitative and quantitative data. APPROACH: The McMaster Modular Assessment Program (McMAP), implemented in McMaster University's Royal College EM residency program in 2011-2012, is a programmatic assessment system that collects and aggregates data from 42 WBA instruments aligned with EM tasks and mapped to the CanMEDS competency framework. These instruments incorporate task-specific checklists, behaviorally anchored task-specific and global performance ratings, and written comments. They are completed by faculty following direct observation of residents during shifts. The rotation preceptor uses aggregated data to complete an end-of-rotation report for each resident in the form of a qualitative global assessment of performance. OUTCOMES: The quality of end-of-rotation reports-as measured by comparing report quality one year prior to and one year after McMAP implementation using the Completed Clinical Evaluation Report Rating tool-has improved significantly (P < .001). This may be a result of basing McMAP's end-of-rotation reports on robust documentation of performance by multiple raters throughout a rotation rather than relying on a single faculty member's recall at rotation's end as in the previous system. NEXT STEPS: By aligning theory-based assessment instruments with authentic EM work-based tasks, McMAP has changed the residency program's culture to normalize daily feedback. Next steps include determining how to handle "big data" in assessment and delineating policies for promotion decisions.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".