Merck Animal Health Veterinary Team study reveals factors associated with well-being, burnout, and mental health among nonveterinarian practice team members
Bibliographic record
Abstract
OBJECTIVE: To assess the levels of burnout, well-being, and mental health of nonveterinarian employees of veterinary practices and, for context, compare them to veterinarians and the general population by use of validated instruments. METHODS: An online survey of 2,271 nonveterinary practice employees drawn from members of the North American Veterinary Technicians Association, members of the Veterinary Hospital Managers Association, referrals from veterinarian respondents to a companion survey, and a large hospital group that owns several hundred US veterinary practices. The study was fielded from September 11 to October 9, 2023. RESULTS: A majority of practice team members were satisfied with their work in veterinary medicine. However, serious psychological distress was twice as prevalent among team members as among veterinarians and well-being was lower than that of veterinarians. Burnout was similar to veterinarians. Personality played a role: team members on average were more likely to score higher in neuroticism than veterinarians and the general population, and neuroticism was a predictor of low well-being, poor mental health, and burnout. There was also evidence of substantial financial stress among team members. CONCLUSIONS: Serious psychological distress was common among practice team members. Financial stress may play a role. Burnout and low levels of well-being were also common. CLINICAL RELEVANCE: This study provided a useful profile of the psychological conditions that many practice employees may be experiencing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".