Twelve tips to optimize group decision-making in medical education: ‘Tipping’ the scales toward wisdom of the crowd and minimizing groupthink
Bibliographic record
Abstract
Group decision-making is now common in medical education, often used for decisions that are both complex and high stakes, such as determining whether to promote or remediate a trainee. In this context, it is often assumed that group decision making is superior to that of an individual, resulting in high quality decision outcomes through the pooling of collective knowledge and experience. Yet, while groups can outperform individuals, this is not guaranteed. In fact, groups are vulnerable to several cognitive biases and process issues that individuals are not subject to and these can lead to poor quality decision outcomes if not managed. As educational leaders who participate in group decision-making, we believe it is our responsibility to ensure the quality of these complex and high-stakes decisions. In this article, we discuss both the potential benefits and vulnerabilities of group decision-making by introducing the concepts of wisdom of the crowd and groupthink, respectively. With this foundation, we then offer twelve evidence-based tips that can be easily implemented in educational group decision-making to minimize groupthink and leverage the wisdom of the crowd.
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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 | 0.101 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".