Adapting the World Health Organization’s Surgical Safety Checklist to High-Income Settings: A Hybrid Effectiveness-Implementation Trial Protocol
Why this work is in the frame
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Bibliographic record
Abstract
Objectives: The proposed study aims to assess users' perceptions of a surgical safety checklist (SSC) reimplementation toolkit and its impact on SSC attitudes and operating room (OR) culture, meaningful checklist use, measures of surgical safety, and OR efficiency at 3 different hospital sites. Background: The High-Performance Checklist toolkit (toolkit) assists surgical teams in modifying and implementing or reimplementing the World Health Organization's SSC. Through the explore, prepare, implement, and sustain implementation framework, the toolkit provides a process and set of tools to facilitate surgical teams' modification, implementation, training on, and evaluation of the SSC. Methods: A pre-post intervention design will be used to assess the impact of the modified SSC on surgical processes, team culture, patient experience, and safety. This mixed-methods study includes quantitative and qualitative data derived from surveys, semi-structured interviews, patient focus groups, and SSC performance observations. Additionally, patient outcome and OR efficiency data will be collected from the study sites' health surveillance systems. Data analysis: Statistical data will be analyzed using Statistical Product and Service Solutions, while qualitative data will be analyzed thematically using NVivo. Furthermore, interview data will be analyzed using the Consolidated Framework for Implementation Research and reach, effectiveness, adoption, implementation, maintenance implementation frameworks. Setting: The toolkit will be introduced at 3 diverse surgical sites in Alberta, Canada: an urban hospital, university hospital, and small regional hospital. Anticipated impact: We anticipate the results of this study will optimize SSC usage at the participating surgical sites, help shape and refine the toolkit, and improve its usability and application at future sites.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it