Factors Causing Variation in World Health Organization Surgical Safety Checklist Effectiveness—A Rapid Scoping Review
Bibliographic record
Abstract
INTRODUCTION: This review was conducted to determine what factors might be responsible for prejudicing the outcomes after the implementation of a World Health Organization Surgical Safety Checklist (WHO SSC), grouping them appropriately and proposing strategies that enable the SSC a more helpful and productive tool in the operating room. METHODS: It was a rapid scoping review conducted as per Preferred Reporting Items for Systematic Review and Meta-analyses extension guidelines for scoping reviews (PRISMA-Scr). Comprehensive search on MEDLINE and Embase was carried out, to include all relevant studies published during last 5 years. Twenty-seven studies were included in analysis. The barriers to SSC implementation were classified into 5 main groups, with further subdivisions in each. RESULTS: The results of review revealed that there are 5 major barriers to SSC at the following levels: organizational, checklist, individual, technical, and implementation. Each of these major barriers, on further evaluation, was found to have more than one contributing factors. All these factors were analyzed individually. CONCLUSIONS: This rapid scoping review has consolidated data, which may pave the way for experts to further examine steps that might be taken locally or globally in order that the WHO SSC to successfully achieve all its desired goals.
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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 | 0.119 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".