Thriving or surviving? A latent profile analysis of resilience, psychological wellbeing and professional quality of life in early‐career veterinarians in Canada
Bibliographic record
Abstract
BACKGROUND: Various adverse mental health outcomes (e.g., burnout) have been reported and shown to impact the longevity of veterinarians' careers, especially during the early career. Both compassion fatigue (CF) and compassion satisfaction (CS) are significant predictors of burnout. Increasing attention is being paid to positive psychology, including psychological wellbeing (PWB) and resilience, as they have the potential to enhance wellbeing in the profession. The objectives of this research were to measure various psychological outcomes of newly graduated veterinarians in Canada and identify underlying profiles based on empirical data. METHODS: An online questionnaire with validated psychometric scales was distributed to graduates of all five Canadian veterinary schools in 2022 and 2023. RESULTS: Latent profile analysis (LPA) (n = 189) revealed two profiles, interpreted as follows: thriving (n = 116; high PWB, CS and resilience, and low burnout and CF) and surviving (n = 73; low PWB, CS and resilience, and high burnout and CF). LIMITATIONS: The sample size was smaller than typically recommended for LPA. CONCLUSION: Our findings revealed that 61% (116/191) of newly graduated veterinarians were considered to have good mental wellbeing or were 'thriving'. Our study amplifies the need for more research on positive wellbeing outcomes and interventions to strengthen veterinary students' and veterinarians' wellbeing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".