Predictors of Professional Quality of Life in Veterinary Professionals
Bibliographic record
Abstract
Working in the veterinary profession can be both stressful and rewarding. High workloads, long work hours, emotionally charged interactions with clients, and exposure to animal suffering and participation in euthanasia place many at risk of compassion fatigue, which then threatens their professional quality of life (ProQOL). Despite this risk, many veterinary professionals choose to stay within the profession. This study explores personal and organizational factors predicting compassion satisfaction (CS), burnout, and secondary traumatic stress (STS) in veterinary professionals, and the extent to which these aspects of ProQOL are linked with intentions to leave the profession. Regression results show that personal factors accounted for 31.1% of the variance in CS, 45.3% in burnout, and 33.8% in STS. Organizational factors significantly accounted for 33.3% of the variance in CS, 47.9% in burnout, and 32.7% in STS. Together, ProQOL accounted for 28.9% and 16.0% of the variance in intentions to leave one's current role and to leave the profession altogether, respectively. These results suggest that both personal and organizational factors play a role in veterinary professionals' ProQOL and highlight the importance of promoting CS and managing burnout and STS for the purpose of fostering veterinary staff well-being and retention.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".