Comparing Individual Versus Team Decision-Making Using Simulated Exercises in a Master of Public Health Program
Bibliographic record
Abstract
In line with the complex modern health care system and the increasing importance of interprofessional teams, a powerful strategy to facilitate the acquisition of essential teamwork skills and expose students to complex decision-making processes is learning in teams. The purpose of our study was to obtain empirical evidence of superior decision-making by teams versus individuals in two simulated decision-making exercises conducted 4 months apart. We collected quantitative data from three cohorts of Master of Public Health students to determine if teams make better decisions than individuals (“team effect”) between September and January. Students completed simulated emergency survival exercises requiring them to make correct decisions individually and then as teams. Decision quality was determined by comparison to survival experts’ decisions. We calculated the “team effect” as the gain or loss of mean individual versus group scores across 10 learning teams per cohort for fall and winter exercises. All three cohorts had a consistently small average team effect in September and a much larger team effect in January. Our study showed consistent improvements in decision-making after students had worked in teams for 4 months. Overall, this study demonstrates the potential benefit of incorporating team learning into a public health curriculum and the importance of strategies to teach teamwork in health education. Using simulation in health education and promoting team learning activities can help prepare students for interprofessional collaboration, a part of the demanding public health landscape. These results might help convince students of the benefits of teamwork, facilitate collaborative decision-making, and enhance the learning experience.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".