A qualitative study exploring the perceived effects of veterinarians' mental health on provision of care
Bibliographic record
Abstract
Introduction: Veterinary medicine is a rewarding, yet demanding profession with a myriad of occupational stressors that can impact the mental health of veterinarians. Stress, mental health outcomes, and associated risk factors amongst veterinarians have been well-researched. Much less research has investigated how high stress and/or poor mental health can impact veterinarians' provision of care. Methods: One-on-one research interviews were conducted with 25 veterinarians at a Canadian veterinary conference in July 2016 and verbatim transcripts were produced from the audio recordings. The research team collaboratively analyzed the interviews using thematic analysis. Results: Five themes described the perceived impacts of high stress and/or poor mental health: perceived negative impacts on interactions with (1) co-workers and (2) clients; (3) reduced concentration; (4) difficulty in decision making; and (5) reduced quality of care. Discussion: These results highlight the perceived impacts of self-reported high stress and/or poor mental health on veterinary team dynamics, the potential to impact case outcomes, and possibly endanger patient safety. Interventions to help mitigate the impacts of high stress and poor mental health are discussed.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".