An In-Depth Exploration into the Occupational Stressors Impacting Veterinarians’ Mental Health and the Perceived Impacts of Mental Health on Veterinarians’ Provision of Care
Bibliographic record
Abstract
Veterinarians are trained to protect the well-being of animals, clients, and the community. Unfortunately, veterinarians encounter occupational stressors that can adversely impact their mental health. Using Canadian veterinarians’ in-depth perspectives, nine occupational stressor themes were reported: 1) nature of the profession; 2) veterinary relationships; 3) client interactions; 4) personal finances; 5) new veterinarian strain; 6) practice owner strain; 7) onus of responsibility; 8) self-described personal characteristics, and 9) moral stressors and moral distress, alongside the implications of the stressors. The perceived impacts of poor mental health upon veterinarians’ provision of care were also explored. This thesis highlights participants’ perceived implications of veterinarians experiencing poor mental health, including negatively impacting colleagues and clients, reduced concentration, difficulty making decisions, and decreased quality of care. In addition, clinic-wide recommendations were also provided to foster a supportive veterinary team, prioritize mental health, and provide consistent and quality veterinary 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".