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 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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.020 |
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".