Global Survey of Perceptions of the Surgical Safety Checklist Among Medical Students, Trainees, and Early Career Providers
Bibliographic record
Abstract
BACKGROUND: The Surgical Safety Checklist (SSC) has been shown to reduce perioperative complications across global health systems. We sought to assess perceptions of the SSC and suggestions for its improvement among medical students, trainees, and early career providers. METHODS: From July to September 2019, a survey assessing perceptions of the SSC was disseminated through InciSioN, the International Student Surgical Network comprising medical students, trainees, and early career providers pursuing surgery. Individuals with ≥2 years of independent practice after training were excluded. Respondents were categorized according to any clinical versus solely non-clinical SSC exposure. Logistic regression was used to evaluate associations between clinical/non-clinical exposure and promoting future use of the SSC, adjusting for potential confounders/mediators: training level, human development index, and first perceptions of the SSC. Thematic analysis was conducted on suggestions for SSC improvement. RESULTS: Respondent participation rate was 24%. Three hundred and eighteen respondents were included in final analyses; 215 (67%) reported clinical exposure and 190 (60%) were promoters of future SSC use. Clinical exposure was associated with greater odds of promoting future SSC use (aOR 1.81 95% CI [1.03-3.19], p = 0.039). A greater proportion of promoters reported "Improved Operating Room Communication" as a goal of the SSC (0.21 95% CI [0.15-0.27]-vs.-0.12 [0.06-0.17], p = 0.031), while non-promoters reported the SSC goals were "Not Well Understood" (0.08 95% CI [0.03-0.12]-vs.-0.03 [0.01-0.05], p = 0.032). Suggestions for SSC improvement emphasized context-specific adaptability and earlier formal training. CONCLUSIONS: Clinical exposure to the SSC was associated with promoting its future use. Earlier formal clinical training may improve perceptions and future use among medical students, trainees, and early career providers.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".