"With every big outbreak, we lose staff": The mental health impacts of depopulation on veterinarians.
Bibliographic record
Abstract
Background: Depopulation, involving the mass euthanasia of livestock, is an established practice in the agricultural industry to ensure animal welfare, human health, and economic stability by preventing disease spread. There is evidence that veterinarians involved in animal-disease management and depopulation experience significant and long-lasting mental health impacts. Objective: This study examined the mental health impacts of depopulation on veterinarians and the ways to build their resilience to these stressful events. Procedure: Using qualitative methods, 11 veterinarians and industry experts from Alberta participated in semi-structured, one-on-one interviews between April 2023 and April 2024. Results: trauma of the event), emotional detachment, and occupational distress (including emotional exhaustion, decreased job satisfaction and turnover intentions, and post-traumatic stress disorder symptoms). These themes were used to adapt the emergency-management framework for veterinarians involved in depopulation, to support their mental health and well-being. Conclusion and clinical relevance: The pervasive behavioral and mental health challenges associated with depopulation highlight the necessity for education, training, support, and policy adjustments to safeguard veterinarians' mental health and well-being.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| 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".