Association of demographic, career, and lifestyle factors with resilience and association of resilience with mental health outcomes in veterinarians in Canada
Bibliographic record
Abstract
OBJECTIVE: To investigate the association of demographic, career, and lifestyle factors with resilience and the association of resilience with mental health outcomes in Canadian veterinarians. SAMPLE: 1,130 veterinarians in clinical practice across Canada. PROCEDURES: An online questionnaire was used to collect participant data and included 5 validated psychometric scales to evaluate resilience (through the Connor-Davidson Resilience Scale [CD-RISC]), perceived stress (through the Perceived Stress Scale), emotional distress (through the Hospital Anxiety and Depression Scale), burnout (through the Maslach Burnout Inventory), and secondary traumatic stress (through the Professional Quality of Life Scale). A multivariable linear regression model was used to investigate associations between CD-RISC scores and demographic, career, and lifestyle characteristics. Univariable linear regression models were used to assess the relationship between resilience scores and other mental health outcomes. RESULTS: The strongest positive association was between CD-RISC score and overall health. The level of satisfaction with support from friends and workplace resources had positive associations with the CD-RISC score. The presence of mental illness had the strongest negative association with the CD-RISC score. Being married, working in a small animal practice, or having an associate role were negatively associated with the CD-RISC score. The CD-RISC score had negative associations with scores for perceived stress, anxiety, depression, burnout, and secondary traumatic stress. CONCLUSIONS AND CLINICAL RELEVANCE: Models provided evidence for the role of resilience in protecting against negative mental health outcomes in veterinarians. Both personal and workplace factors were associated with resilience, presenting opportunities for intervention at each of these levels.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".