A Survey of Injuries That Occurred in Veterinary Teaching Hospitals during 2017
Bibliographic record
Abstract
Knowing the frequency, extent or severity of injuries that occur to students and staff within veterinary teaching hospitals (VTHs) is necessary for proactive management of their safety. This study surveyed contemporaneously-captured incident reports likely to cause or causing injury to students and staff of veterinary teaching hospitals in Europe, the United States of America (USA), Canada, Australia, and New Zealand, recorded in 2017. Four different severities of incident were evaluated within four different cohorts of people, precipitated by five categories for cause. Within each cause-category, further subdivision was based on the nature of the incident. All colleges of veterinary medicine accredited by the American Veterinary Medical Association (AVMA) Council on Education (COE) or the Australasian Veterinary Boards Council were surveyed. Responses were received from (7/7, 100%) schools in Australia and New Zealand, 12/30 (40%) the United States of America, 1/4 (25%) Canada, 1/1 (100%) Mexico, and 1/3 (33%) United Kingdom, and no responses were received from the AVMA-COE accredited schools in the European Union. The mean incidence of incidents caused by horses was (0.4/1,000 cases), followed by food animals (0.1/1,000 cases), other animals (0.1/1,000 cases), and small animals (0.074/1,000 cases). Within veterinary teaching hospitals at many institutions, veterinary students and veterinarians are particularly at risk of injuries caused by hand-held instruments, such as scalpels and needles. Non-veterinary staff are more at risk than students or veterinarians from injuries caused by small animals. Recording and reporting of incidents is not uniform and may be lacking in detail. Some institutions' systems for record management preclude easy evaluation, and therefore may be insufficient for proactive management of health and safety as required by accreditation bodies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".