Using i-PARIHS to assess implementation of the Surgical Safety Checklist: an international qualitative study
Bibliographic record
Abstract
BACKGROUND: Strategies selected to implement the WHO's Surgical Safety Checklist (SSC) are key factors in its ability to improve patient safety. Underutilization of implementation frameworks for informing implementation processes hinders our understanding of the checklists' varying effectiveness in different contexts. This study explored the extent to which SSC implementation practices could be assessed through the i-PARIHS framework and examined how it could support development of targeted recommendations to improve SSC implementation in high-income settings. METHODS: This qualitative study utilized interviews with surgical team members and health administrators from five high-income countries to understand the key elements necessary for successful implementation of the SSC. Using thematic analysis, we identified within and across-case themes that were mapped to the i-PARIHS framework constructs. Gaps in current implementation strategies were identified, and the utility of i-PARIHS to guide future efforts was assessed. RESULTS: Fifty-one multi-disciplinary clinicians and health administrators completed interviews. We identified themes that impacted SSC implementation in each of the four i-PARIHS constructs and several that spanned multiple constructs. Within innovation, a disconnect between the clinical outcomes-focused evidence in the literature and interviewees' patient-safety focus on observable results reduced the SSC's perceived relevance. Within recipients, existing surgical team hierarchies impacted checklist engagement, but this could be addressed through a shared leadership model. Within context, organizational priorities resulting in time pressures on surgical teams were at odds with SSC patient safety goals and reduced fidelity. At a health system level, employing surgical team members through the state or health region resulted in significant challenges in enforcing checklist use in private vs public hospitals. Within its facilitation construct, i-PARIHS includes limited definitions of facilitation processes. We identified using multiple interdisciplinary champions; establishing checklist performance feedback mechanisms; and modifying checklist processes, such as implementing a full-team huddle, as facilitators of successful SSC implementation. CONCLUSION: The i-PARIHS framework enabled a comprehensive assessment of current implementation strategies, identifying key gaps and allowed for recommending targeted improvements. i-PARIHS could serve as a guide for planning future SSC implementation efforts, however, further clarification of facilitation processes would improve the framework's utility. TRIAL REGISTRATION: No health care intervention was performed.
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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 | 0.048 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".