Personality traits and workplace factors predict professional quality of life among companion-animal veterinary professionals
Bibliographic record
Abstract
Objective: To assess the prevalence of burnout (BO), secondary traumatic stress (STS), and compassion satisfaction (CS) and identify associated individual, clinic, and dog-handling factors among veterinary professionals. Methods: A cross-sectional online questionnaire was distributed to veterinary professionals in Canada and US (2023 to 2024). The questionnaire collected individual, clinic, and dog-handling information and measured ProQOL (BO, STS, CS). Logistic regression models examined associations between these factors and ProQOL. Results: Participants (n = 691) had moderate BO (71.2%), STS (71.8%), and CS (74.3%); 2.4% reported high STS, and none had high BO. Veterinarians had lower odds of moderate/high BO (OR, 0.50; 95% CI, 0.32 to 0.78) and CS (OR, 0.12; 95% CI, 0.021 to 0.64) compared with nonveterinarians. Below-normal personality traits were associated with moderate/high BO and/or STS: extraversion (BO: OR, 2.25; 95% CI, 1.47 to 3.46), agreeableness (BO: OR, 2.02; 95% CI, 1.29 to 3.18; STS: OR, 1.60; 95% CI, 1.07 to 2.39), conscientiousness (BO: OR, 3.91; 95% CI, 2.41 to 6.34; STS: OR, 3.81; 95% CI, 2.47 to 5.88), emotional stability (BO: OR, 1.96; 95% CI, 1.24 to 3.11), and openness (BO: OR, 1.64; 95% CI, 1.05 to 2.56; STS: OR, 1.88; 95% CI, 1.26 to 2.81). Stress-reducing certification was associated with moderate/high BO (OR, 2.04; 95% CI, 1.14 to 3.64). Conclusions: Personality traits and individual factors were associated with ProQOL, whereas handling techniques were not. Clinical Relevance: Findings provide exploratory evidence for workplace strategies to reduce BO and STS and enhance CS while generating hypotheses for future intervention research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".