Impact of Checklist Use on Wellness and Post-Elective Surgery Appointments in a Veterinary Teaching Hospital
Bibliographic record
Abstract
Cognitive functioning is often compromised with increasing levels of stress and fatigue, both of which are often experienced by veterinarians. Many high-stress fields have implemented checklists to reduce human error. The use of these checklists has been shown to improve the quality of medical care, including adherence to evidence-based best practices and improvement of patient safety. Although it has been recognized that veterinary medicine would likely demonstrate similar benefits, there have been no published studies to date evaluating the use of checklists for improving quality of care in veterinary medicine. The purpose of the current study was to evaluate the impact of checklists during wellness and post-elective surgery appointments conducted by fourth-year veterinary students within their Community Practice rotation at a US veterinary teaching hospital. Students were randomly assigned to one of two groups: those who were specifically asked to use the provided checklists during appointments, and those who were not asked to use the checklists but had them available. Two individuals blinded to the study reviewed the tapes of all appointments in each study group to determine the amount and type of medical information offered by veterinary students. Students who were specifically asked to use the checklists provided significantly more information to owners, with the exception of keeping the incision clean. Results indicate the use of checklists helps students provide more complete information to their clients, thereby potentially enhancing animal care.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".