Surgical Teams’ Attitudes About Surgical Safety and the Surgical Safety Checklist at 10 Years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it