Perceptions of Operating Room Tension across Professions: Building Generalizable Evidence and Educational Resources
Bibliographic record
Abstract
BACKGROUND: Effective team communication is critical in health care, yet no curriculum exists to teach it. Naturalistic research has revealed systematic patterns of tension and profession-specific interpretation of operating room team communication. Replication of these naturalistic findings in a controlled, video-based format could provide a basis for formal curricula. METHOD: Seventy-two surgeons, nurses, and anesthesiologists independently rated three video-based scenarios for the three professions' level of tension, responsibility for creating tension and responsibility for resolution. Data were analyzed using three-way, mixed-design analyses of variance. RESULTS: The three professions rated tension levels of the various scenarios similarly (F=1.19, ns), but rated each profession's responsibility for creating (F=2.86, p<.05) and resolving (F=1.91, p<.01) tension differently, often rating their profession as having relatively less responsibility than the others. CONCLUSIONS: These results provide an evidence base for team communications training about tension patterns, disparity of professional perspectives, and implications for team function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".