Failure to Acknowledge High Suicide Risk among Veterinarians
Bibliographic record
Abstract
A high suicide risk has been reported among veterinarians in comparison to the general population. Postulated causes have included depression, substance abuse, work-related stress, reluctance to admit psychiatric problems, and access to lethal drugs and/or familiarity with euthanasia. Members of the Student Chapter of the American Veterinary Medical Association (AVMA), all veterinarians licensed in Alabama, and all US veterinary-association executive directors were surveyed regarding their attitudes concerning mental health issues, including veterinarian suicide. Only 10% of veterinary student respondents (N=58) believed that suicide risk is higher among veterinarians than in the general population. Of the 22 state associations' executive directors who participated in the survey, 37% believed that suicide is a significant concern for veterinarians and only 44% indicated that a veterinary wellness program was available in their respective states. Of the 1,455 licensed veterinarians in Alabama, 701 responded to the survey; 11% of respondents believed that suicide among veterinarians was a problem. In addition, 66% of respondents indicated that they had been "clinically depressed," but 32% of those with depression had not sought treatment. More females (27%) than males (20%) admitted that they had "seriously considered suicide" (p<.01). Female veterinarians were more likely than male veterinarians (15% versus 7%) to indicate that they were "not sure they'd made the right career choice" (p<.001), and 4% of all respondents indicated "definitely not being happy with their career." It is of concern that veterinarians not only have a higher risk of suicide but that they also have fewer support structures. The wide discrepancies between the published risk of suicide for veterinarians and their own views of their risk suggests an inadequate awareness of their own mental health vulnerability which could put them at higher risk.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".