Games as teaching tools in a surgical residency
Bibliographic record
Abstract
BACKGROUND: Didactic lectures have been the mainstay of core teaching in the surgical residency program at our school. Our concerns about the educational impact of these passive activities led us to consider more interactive teaching approaches. METHODS: We developed an interactive games-based approach to learning. One set of games was labeled "Who wants to be a Surgeon" (WS) and the other was called "Senior Face-off" (SF). We evaluated the impact of this innovation using an end-of-year questionnaire. RESULTS: Enjoyment, teaching quality and preference over lectures were high for both games. However, the WS sparked interest significantly more in junior residents (4.3 +/- 0.21 vs 3.3 +/- 0.31, p = 0.015) and senior residents found both games more stressful than did junior residents (WS: 2.88 +/- 0.32 vs 2.00 +/- 0.21, p = 0.038, and SF: 3.54 +/- 0.29 vs 1.80 +/- 0.33, p = 0.001). CONCLUSIONS: This innovative teaching technique promoted learner interest and was regarded as a worthwhile educational activity. Games with a competitive emphasis may unduly stress senior residents.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".