Development of a cumulative teaching score for tracking surgeon performance in undergraduate medical education
Bibliographic record
Abstract
Background: Surgeon educators are important in undergraduate medical education (UME). However, teaching activities are undervalued and under-recognized compared with research, resulting in poorer quantity and quality of surgeon teaching. The purpose of this study was to investigate teaching roles available to surgeons and the amount of effort involved. Methods: A comprehensive review of all possible roles surgeons may take in UME at our institution was assembled. Delphi committee members were asked to evaluate each teaching role on the amount of effort needed per hour. Results were analyzed using descriptive statistics, and a Cronbach α of 0.60 or higher was the threshold to declare consensus. Results: Twenty-five participants, including physicians, residents and medical students, completed the study. Consensus was reached on the amount of effort needed for each teaching role. These values were used to prototype a cumulative teaching score that can be used to qualitatively quantify surgeon teaching. Conclusion: Surgeon teaching is important in UME, but not tracked and thus not valued. To improve the quantity and quality of surgeon teaching in UME, we need to track, reward and recognize surgeon teaching activities. The “effort score” we developed to objectively and transparently qualify teaching was able to determine the relative effort needed for each teaching activity in UME at the University of Toronto. Combining the effort score and time committed to each teaching activity will produce a cumulative teaching score for each instructor.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".