Are we at risk of groupthink in our approach to teamwork interventions in health care?
Bibliographic record
Abstract
CONTEXT: The incidence of medical error, adverse clinical events and poor quality health care is unacceptably high and there are data to suggest that poor coordination of care, or teamwork, contributes to adverse outcomes. So, can we assume that increased collaboration in multidisciplinary teams improves performance and health care outcomes for patients? METHODS: In this essay, the authors discuss some reasons why we should not presume that collective decision making leads to better decisions and collaborative care results in better health care outcomes. RESULTS: Despite an exponential increase in interventions designed to improve teamwork and interprofessional education (IPE), we are still lacking good quality data on whether these interventions improve important outcomes. There are reasons why some of the components of 'effective teamwork', such as shared mental models, team orientation and mutual trust, could impair delivery of health care. For example, prior studies have found that brainstorming results in fewer ideas rather than more, and hinders rather than helps productivity. There are several possible explanations for this effect, including 'social loafing' and cognitive overload. Similarly, attributes that improve cohesion within groups, such as team orientation and mutual trust, may increase the risk of 'groupthink' and group conformity bias, which may lead to poorer decisions. CONCLUSIONS: In reality, teamwork and IPE are not inherently good, bad or neutral; instead, as with any intervention, their effect is modified by the persons involved, the situation and the interaction between persons and situation. Thus, rather than assume better outcomes with teamwork and IPE interventions, as clinicians and educators we must demonstrate that our interventions improve the delivery of health care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".