Surgical Teams’ Attitudes About Surgical Safety and the Surgical Safety Checklist at 10 Years
Bibliographic record
Abstract
To assess health care professionals' attitudes on the Surgical Safety Checklist ("the Checklist") in resource-rich health systems and provide insights on strategies for optimizing Checklist use. Background: In use for over a decade, the Checklist is a safety instrument aimed at improving operating room communication, teamwork, and evidence-based safety practices. Methods: An online survey was sent to surgeons, nurses, and anesthesiologists in 5 high-income countries (Canada, the United States, the United Kingdom, Australia, and New Zealand). Survey results were analyzed using SPSS. Results: A total of 2032 health care professionals completed the survey. Of these respondents, 47.6% were nurses, 70.5% were women, 65.1% were from the United States, and 50.0% had 20 years of experience or more in their role. Most respondents felt the Checklist positively impacted patient safety (70.9%), team communication (73.1%), and teamwork (58.9%). Only 50.3% of respondents were satisfied their team's use of the Checklist, and only 47.5% reported team members stopping to fully participate in the process. More nurses lacked confidence regarding their role in the Checklist process than surgeons and anesthesiologists combined (8.9% vs 4.3%). Fewer surgeons and anesthesiologists than nurses felt they received adequate training on the Checklist's use (57.8% vs 76.7%). Conclusions: While most respondents perceive the Checklist as enhancing patient safety, not all surgical team members are actively engaging with its use. To enhance buy-in and meaningful use of the Checklist, health systems should provide more training on the Checklist with respect to its purpose and strengthening teamwork.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".