Systematic review and meta-analysis of veterinary-related occupational exposures to hazards
Bibliographic record
Abstract
ABSTRACT Objective Understanding hazards within the veterinary profession is critical for developing strategies to ensure health and safety in the work environment. This study was conducted to systematically review and synthesize data on reported risks within veterinary workplaces. Methods A systematic review of published data reporting occupational hazards and associated risk factors were searched within three database platforms namely PubMed, Ebscohost, and Google scholar. To determine the proportion estimates of hazards and pooled odds ratio, two random-effects meta-analysis were performed. Results Data showed veterinarians and students were at high risk of exposure to diverse physical, chemical, and biological hazards. For the biological, chemical and physical hazards, the pooled proportion estimates were 17% (95% CI: 15.0–19.0, p < 0.001), 7.0% (95% CI: 6.0–9.0%, p < 0.001) and 65.0% (95% CI: 39.0–91.0%, p < 0.001) respectively. A pooled odds ratio indicated the odds of physical (OR = 1.012, 95% CI: 1.008–1.017, p < 0.001) and biological exposures (OR = 2.07, 95% CI: 1.70–2.52, p < 0.001) increased more when working with or in contact with animals than non-contact. Conclusions This review has provided a better understanding of occupational health and safety status of veterinarians and gaps within the developing countries. Veterinarians including students are at considerable risk of occupational-related hazards. The need to improve government and organisation policies and measures on occupational health and safety is therefore crucial, most importantly in Africa.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".