The World Health Organization's ‘Surgical Safety Checklist’: should evidence-based initiatives be enforced in hospital policy?
Bibliographic record
Abstract
OBJECTIVES: To assess the awareness and voluntary usage of the World Health Organization's Surgical Safety Checklist (WHO SSC), just prior to its mandatory implementation. DESIGN: Questionnaire-based, prospective, telephone survey. SETTING: Patients are exposed to systematic risks and principles of surgical safety are inconsistently applied even in sophisticated settings. The evidence-based WHO SSC addresses shortfalls to promote patient safety. It was formally introduced in the United Kingdom in January 2009 and became a mandatory preoperative requirement in all hospitals from February 2010. PARTICIPANTS: Two hundred and thirty-eight hospitals, both private and government-run, in the UK. MAIN OUTCOME MEASURES: Appreciation among senior theatre personnel as to the existence, implementation and usage of the WHO SSC concept. RESULTS: Almost all had heard of the SSC, but in only two-thirds of hospitals was its use mandatory. Where the SSC was not compulsory, 80% were using it informally or sporadically. One-quarter of senior theatre personnel in hospitals without compulsory use indicated they did not know or that their department did not plan on using the checklist in the next six months, despite the deadline for implementation. CONCLUSIONS: If the SSC is to optimize safety, then greater education and awareness is required.
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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 | 0.177 | 0.399 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.008 |
| 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".