Team Stress and Its Impact on Interprofessional Teams: A Narrative Review
Bibliographic record
Abstract
Phenomenon: Interprofessional healthcare team (IHT) collaboration can produce powerful clinical benefits for patients; however, these benefits are difficult to harness when IHTs work in stressful contexts. Research about stress in healthcare typically examines stress as an individual psychological phenomenon, but stress is not only a person-centered experience. Team stress also affects the team’s performance. Unfortunately, research into team stress is limited and scattered across many disciplines. We cannot prepare future healthcare professionals to work as part of IHTs in high-stress environments (e.g., emergency medicine, disaster response) unless we review how this dispersed literature is relevant to medical education. Approach: The authors conducted a narrative review of the literature on team stress experienced by interprofessional teams. The team searched five databases between 1 Jan 1990 and 16 August 2021 using the search terms: teams AND stress AND performance. Guided by four research questions, the authors reviewed and abstracted data from the 22 relevant manuscripts. Findings: Challenging problems, time pressure, life threats, environmental distractors, and communication issues are the stressors that the literature reports that teams faced. Teams reacted to team stress with engagement/cohesion and communication/coordination. Stressors impact team stress by either hindering or improving team performance. Critical thinking/decision-making, team behaviors, and time for task completion were the areas of performance affected by team stress. High-quality communication, non-technical skills training, and shared mental models were identified as performance safeguards for teams experiencing team stress. Insights: The review findings adjust current models explaining drivers of efficient and effective teams within the context of interprofessional teams. By understanding how team stress impacts teams, we can better prepare healthcare professionals to work in IHTs to meet the demands placed on them by the ever-increasing rate of high-stress medical situations.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".