Refining Successful Implementation Strategies for the Surgical Safety Checklist in High-Income Contexts: Results of an International Mixed Methods Study
Bibliographic record
Abstract
The WHO Surgical Safety Checklist (SSC) continues to show inconsistent success in reducing surgical complications in high-income settings. Previous implementation research identified potential barriers and facilitators to success, but it primarily consists of qualitative studies with small sample sizes in limited geographic areas. We conducted a multi-country mixed-methods study of barriers and facilitators to SSC implementation to better inform policies and practices for improving SSC buy-in and use to maximize its impact. This convergent parallel mixed-methods study utilized survey and interview data from surgical team members practicing in five countries. Survey data were analyzed using χ2 analysis or Fisher’s exact test for categorical variables and McNemar’s test to analyze differences between related groups for dichotomous variables. Interview data underwent inductive coding followed by thematic analysis for predominant themes common across the study countries. The study resulted in 2,032 survey responses and 51 interviews. Facilitators to success included having influential multi-disciplinary champions from surgery, anesthesiology, and nursing; using a distributed leadership process to promote ownership across all surgical team members; and providing education on the “why” of the checklist. Practitioners found patient safety metrics (e.g., wrong side surgery) more relevant than clinical outcome measures (e.g., surgical mortality) to assess SSC success. Finally, auditing for process engagement was felt to promote more meaningful use than auditing for checklist completion. Our international examination of barriers and facilitators to successful SSC implementation has identified more specific guidance for high-income settings that integrate people, data, and processes.
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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 | 0.107 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".