Development of Survey Scales for Measuring Exposure and Behavioral Responses to Disruptive Intraoperative Behavior
Bibliographic record
Abstract
OBJECTIVES: Disruptive intraoperative behavior has detrimental effects to clinicians, institutions, and patients. How clinicians respond to this behavior can either exacerbate or attenuate its effects. Previous investigations of disruptive behavior have used survey scales with significant limitations. The study objective was to develop appropriate scales to measure exposure and responses to disruptive behavior. METHODS: We obtained ethics approval. The scales were developed in a sequence of steps. They were pretested using expert reviews, computational linguistic analysis, and cognitive interviews. The scales were then piloted on Canadian operating room clinicians. Factor analysis was applied to half of the data set for question reduction and grouping. Item response analysis and theoretical reviews ensured that important questions were not eliminated. Internal consistency was evaluated using Cronbach α. Model fit was examined on the second half of the data set using confirmatory factor analysis. Content validity of the final scales was re-evaluated. Consistency between observed relationships and theoretical predictions was assessed. Temporal stability was evaluated on a subsample of 38 respondents. RESULTS: A total of 1433 and 746 clinicians completed the exposure and response scales, respectively. Content validity indices were excellent (exposure = 0.96, responses = 1.0). Internal consistency was good (exposure = 0.93, responses = 0.87). Correlations between the exposure scale and secondary measures were consistent with expectations based on theory. Temporal stability was acceptable (exposure = 0.77, responses = 0.73). CONCLUSIONS: We have developed scales measuring exposure and responses to disruptive behavior. They generate valid and reliable scores when surveying operating room clinicians, and they overcome the limitations of previous tools. These survey scales are freely available.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".