Attitudes towards surgical safety checklists among American College of Veterinary Surgeons diplomates
Bibliographic record
Abstract
OBJECTIVE: To examine attitudes towards surgical safety checklists (SSCs) among American College of Veterinary Surgeons (ACVS) diplomates and to identify barriers to implementation. STUDY DESIGN: Qualitative online research survey. SAMPLE POPULATION: A total of 1282 current ACVS diplomates. METHODS: An anonymous online survey was distributed to current ACVS diplomates via email. ACVS diplomates were identified using publicly available data through the ACVS website. A total of 1282 surveys were electronically distributed, and respondents were given 4 weeks to respond. The survey consisted of 34 questions examining (1) demographic information, (2) current use of SSCs, (3) knowledge and attitudes towards SSCs, (4) perceived advantages and disadvantages to use of SSCs, (5) implementation strategies, and (6) potential reasons for noncompletion of SSCs. RESULTS: Survey response rate was 20% (257/1282). A total of 169 of 249 (67.9%) respondents indicated using SSCs. Respondents generally agreed that SSCs were proven to reduce surgical complications (196/249 [78.7%]) and did not perceive any disadvantages to use (100/138 [75.2%]). Respondents not using SSCs were more likely to perceive them as a waste of time (p < .001). The most common reasons for noncompletion of SSCs were forgetfulness (21/52 [39.6%]) and time constraints (19/52 [36.5%]). Improved training (72/138 [52.2%]) and modifying the SSC based on staff feedback (69/138 [50%]) were suggested as methods to improve SSC uptake. CONCLUSION: Respondents currently using SSCs were generally satisfied. Time constraints and memory related issues were common causes for noncompletion of SSCs. CLINICAL SIGNIFICANCE: Efforts to expand the implementation of SSCs in veterinary surgery should focus on improved engagement of relevant stakeholders and modification of the SSC to suit local conditions.
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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 | 0.032 |
| 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 | 0.000 | 0.001 |
| 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".