Knowledge and attitudes towards surgical safety checklists: a survey of veterinary professionals
Bibliographic record
Abstract
OBJECTIVE: To determine the knowledge and use of safe surgical checklists (SSCs) and surgical safety practices (SSPs) in different sectors of veterinary medicine. SAMPLE: 1,235 small animal veterinarians who perform surgery in the United States and Canada. PROCEDURES: An online survey was distributed to veterinarians through social media platforms, specialty listservs, and the Veterinary Information Network. Respondents provided information regarding their role, practice type, as well as knowledge, attitudes, and use of SSCs. Respondents also provided information about performance of SSPs including team introductions; confirmation of antibiotic prophylaxis, patient identity, procedure to be performed; and confirmation of completion of all procedures. RESULTS: A greater proportion of Diplomates of the American College of Veterinary Surgeons (49/77 [64%]) reported using an SSC than other veterinarians (257/1157 [22%]; P < .0001). A greater proportion of veterinarians working in university and multispecialty hospitals reported using a SSC (71/142 [50%]) than in other practice settings (235/1092 [22%]; P < .0001). Use of a SSC correlated with consistent performance of surgical safety practices listed above (P < .0001). Primary care veterinarians commonly reported not using a SSC because they did not know about them (359/590 [61%]). Of the 507 respondents who had ever used a SSC, 333 (66%) believed the checklist had prevented an error or complication. CLINICAL RELEVANCE: Despite widespread knowledge and adoption of SSC use in human medicine, knowledge and use of SSCs is lacking in primary care veterinary practice. Checklist use has previously been shown to decrease post operative complications and in this study was correlated with increased use of SSPs that decreased complications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".