Changes in safety attitude and relationship to decreased postoperative morbidity and mortality following implementation of a checklist-based surgical safety intervention
Bibliographic record
Abstract
OBJECTIVES: To assess the relationship between changes in clinician attitude and changes in postoperative outcomes following a checklist-based surgical safety intervention. DESIGN: Pre- and post intervention survey. SETTING: Eight hospitals participating in a trial of a WHO surgical safety checklist. PARTICIPANTS: Clinicians actively working in the designated study operating rooms at the eight hospitals. SURVEY INSTRUMENT: Modified operating-room version Safety Attitudes Questionnaire (SAQ). MAIN OUTCOME MEASURES: Change in mean safety attitude score and correlation between change in safety attitude score and change in postoperative outcomes, plus clinician opinion of checklist efficacy and usability. RESULTS: Clinicians in the preintervention phase (n=281) had a mean SAQ score of 3.91 (on a scale of 1 to 5, with 5 representing better safety attitude), while the postintervention group (n=257) had a mean of 4.01 (p=0.0127). The degree of improvement in mean SAQ score at each site correlated with a reduction in postoperative complication rate (r=0.7143, p=0.0381). The checklist was considered easy to use by 80.2% of respondents, while 19.8% felt that it took a long time to complete, and 78.6% felt that the programme prevented errors. Overall, 93.4% would want the checklist used if they were undergoing operation. CONCLUSIONS: Improvements in postoperative outcomes were associated with improved perception of teamwork and safety climate among respondents, suggesting that changes in these may be partially responsible for the effect of the checklist. Clinicians held the checklist in high regard and the overwhelming majority would want it used if they were undergoing surgery themselves.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".