Executive summary of the Merck Animal Health Veterinarian Wellbeing Study III and Veterinary Support Staff Study
Bibliographic record
Abstract
OBJECTIVE: Merck Animal Health Veterinarian Wellbeing Study III was conducted to continue to monitor mental health and well being within the veterinary profession in the US and to identify factors associated with high levels of wellbeing and lack of serious psychological distress. METHODS: A questionnaire consisting of several instruments and questions for measurement of mental health and wellbeing was completed by 2,495 veterinarians and 448 veterinary support staff. Results for veterinarians were weighted to the US AVMA membership. RESULTS: This study revealed that wellbeing and mental health of some veterinarians declined over the past 2 years, driven in part by the COVID-19 pandemic and extreme labor shortages. Burnout remained at a high level, but there was no increase in suicide ideation. A new companion survey of veterinary support staff demonstrated that staff scored lower in wellbeing and mental health, and higher in burnout than veterinarians. CLINICAL RELEVANCE: Importantly, these studies identified techniques that both individuals and employers may find useful in fostering wellbeing and good mental health. A healthy method for coping with stress and good work-life balance was important, as was engaging a financial adviser for those with student debt or other financial stresses. Employers should create safe environments where employees feel comfortable seeking help, reducing the stigma associated with mental health issues. In addition, employers can provide Employee Assistance Programs and health insurance that covers mental health treatment. Fostering a healthy work culture was also important, one with good communication, teamwork, trust, and adequate time allotted to provide quality patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.022 |
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".