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Perceptions of Operating Room Tension across Professions: Building Generalizable Evidence and Educational Resources

2005· article· en· W2089237819 on OpenAlexaff
Lorelei Lingard, Glenn Regehr, Sherry Espin, Isabella Devito, Sarah Whyte, Douglas Buller, Bohdan Sadovy, David A. Rogers, Richard K. Reznick

Bibliographic record

VenueAcademic Medicine · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsThe Wilson Centre
Fundersnot available
KeywordsCurriculumPerceptionPsychologyProfessional responsibilityMedical educationCertificationFunction (biology)MedicineNursingPedagogyPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.143
GPT teacher head0.515
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2005
Admission routes1
Has abstractyes

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