Development of an On-Call Assessment Tool for Competency-Based Surgical Training
Bibliographic record
Abstract
Introduction: A central tenet of competency-based medical education is formative assessment of trainees. There are no assessments examining resident competence on-call, despite this being a significant component of resident training and characterized by less supervision compared to daytime.\nMethods: A national survey was conducted to evaluate the state of assessment in Canadian Plastic and Reconstructive Surgery programs. An on-call assessment tool was developed based on a consensus group and was piloted over six months. Validity of the tool was examined through qualitative and quantitative methods.\nResults: There were 63 tools completed across ten residents and seven staff physicians. Tool reliability was 0.67 and scores were significantly correlated to year of training. Staff and residents considered the tool useful, feasible and acceptable.\nConclusions: The on-call assessment tool has multiple sources of validity evidence to support its purpose of assessing surgical resident competence on-call. Further research is required to assess tool generalizability.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".