Multiple Interacting Factors Influence Adherence, and Outcomes Associated with Surgical Safety Checklists: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVES: The surgical safety checklist (SSC) is meant to enhance patient safety but studies of its impact conflict. This study explored factors that influenced SSC adherence to suggest how its impact could be optimized. METHODS: Participants were recruited purposively by profession, region, hospital type and time using the SSC. They were asked to describe how the SSC was adopted, associated challenges, perceived impact, and suggestions for improving its use. Grounded theory and thematic analysis were used to collect and analyse data. Findings were interpreted using an implementation fidelity conceptual framework. RESULTS: Fifty-one participants were interviewed (29 nurses, 13 surgeons, 9 anaesthetists; 18 small, 14 large and 19 teaching hospitals; 8 regions; 31 had used the SC for ≤12 months, 20 for 13+ months). The SSC was inconsistently reviewed, and often inaccurately documented as complete. Adherence was influenced by multiple issues. Extensive modification to accommodate existing practice patterns eliminated essential interaction at key time points to discuss patient management. Staff were often absent or not paying attention. They did not feel it was relevant to their work given limited evidence of its effectiveness, and because they were not engaged in its implementation. Organizations provided little support for implementation, training, monitoring and feedback, which are needed to overcome these, and other individual and team factors that challenged SSC adherence. Responses were similar across participants with different characteristics. CONCLUSIONS: Multiple processes and factors influenced SSC adherence. This may explain why, in studies evaluating SSC impact, outcomes were variable. Recommendations included continuing education, time for pilot-testing, and engaging all staff in SSC review. Others may use the implementation fidelity framework to plan SSC implementation or evaluate SSC adherence. Further research is needed to establish which SSC components can be modified without compromising its effectiveness.
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How this classification was reachedexpand
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".