Work-life balance is essential to reducing burnout, improving well-being
Bibliographic record
Abstract
OBJECTIVE: To assess levels of burnout, well-being, and mental health of veterinarians and compare them to those of nonveterinarians by use of validated instruments, and to identify the predictive values of techniques individuals can use to help reduce burnout and/or improve well-being and mental health. SAMPLE: An online survey of 4,636 veterinarians from a random sample of 40,000 US veterinarians provided by the AVMA. METHODS: The study was fielded from September 11 to October 9, 2023. RESULTS: Burnout and well-being of veterinarians were generally consistent with that of employed US adults. Serious psychological distress was more common among veterinarians than in the general population. Veterinarians on average were more likely to score higher in neuroticism than nonveterinarians, and neuroticism was a predictor of low well-being, poor mental health, and burnout. Work-life balance, an effective coping mechanism for stress, and working in a positive clinic culture were among the significant factors that predicted good well-being and mental health and reduced burnout. CLINICAL RELEVANCE: The higher percentage than the norm of veterinarians with serious psychological distress was a concern. Focusing on maintaining a good work-life balance and adopting a reliable coping mechanism can potentially help reduce distress. Veterinary medicine is an inherently stressful profession. The purpose of this study was to identify key factors that contribute to burnout, well-being, and mental health and to determine what behaviors and management techniques help reduce stress and burnout and contribute to well-being and mental health, thus improving job satisfaction and personal fulfillment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".