Use of a surgical safety checklist after implementation in an academic veterinary hospital
Bibliographic record
Abstract
OBJECTIVE: To determine the use and barriers to uptake of a surgical safety checklist (SSC) after implementation in a veterinary teaching hospital. STUDY DESIGN: Voluntary online survey and retrospective study. SAMPLE POPULATION: All personnel actively involved in the Ontario Veterinary College Health Sciences Centre small animal surgery service between October 2, 2018 and June 28, 2019. METHODS: Surgical case logs and electronically initiated SSC were reviewed to calculate checklist use. The sample population was surveyed to identify factors and barriers associated with use of the SSC. Participants were allowed 1 month to respond, and five reminder emails were sent. RESULTS: Forth-one of 50 (82%) participants completed the survey. The SSC was used in 374 of 784 (47.7%) surgeries. Use rates declined over sequential three-month intervals (P < .0001). Twenty-six of 41 (63%) respondents overestimated checklist use. Staff attitudes were largely supportive of the SSC, with 29 of 41 respondents suggesting mandatory application. Forgetfulness, hierarchal concerns, timing issues, perceived delays in care, lack of clarity regarding roles, and inadequate training were identified as obstacles to use of the SSC. CONCLUSION: The SCC tested in this study was used in approximately half of the surgical procedures performed after its implementation. Hospital personnel were supportive of the SSC; forgetting to use the SSC was the most common barrier identified by respondents (24/41 [59%]). CLINICAL SIGNIFICANCE: The SSC implementation experience and user feedback described here should be taken into consideration to improve design and implementation of future SSC.
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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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".