Practice-wide certification in stress-reducing animal care lowers the rate of patient-inflicted injuries to veterinary staff in small animal general practices
Bibliographic record
Abstract
Objective: The purpose of our study was to investigate the relationship between patient handling techniques and the incidence of patient-inflicted injury to veterinary staff. Furthermore, we aimed to characterize hospitals' postinjury care protocols. Methods: This cross-sectional study was conducted on convenience sample data of small animal general practices in the US and Canada, collected via an online survey. The survey was distributed between October and November 2023. Practices located outside of the US and Canada and those not identified as primary care or general small animal practices were excluded. Results: There were 113 survey responses that met the inclusion criteria. Injury rates were lower in practices where 100% of veterinary staff were certified in some type of stress-reducing care method or program. Practices with < 100% of employees certified were 3.5 times (95% CI, 1.2 to 10.4) more likely to have injuries once a month or more compared to practices with 100% certification. Conclusions: It is possible that training in stress-reducing animal care could make veterinary workplaces safer. Given the burnout and staff retention issues veterinary medicine is facing, as well as the cost of workers' compensation insurance, this study's findings could provide valuable information for veterinary employers. Clinical Relevance: Stress-reducing patient care programs that allow all members of the veterinary staff or the practice as a whole to become certified can lead to a reduction in occupational injury associated with animal handling.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".