The moderating role of different forms of empathy on the association between performing animal euthanasia and career sustainability
Bibliographic record
Abstract
Abstract Veterinarian work may take an emotional toll on practitioners and their mental health, potentially driving premature exit from the profession. Performing animal euthanasia is frequently identified as a potential risk factor for sustainable mental health. Yet, research has demonstrated mixed results between euthanasia performance and detrimental mental health outcomes, suggesting the potential for factors that moderate this association. In this three‐wave longitudinal survey study, including 110 currently practicing veterinarians (88% female), we examined whether the type of empathy experienced by these practitioners plays a role in the association between performing animal euthanasia and career sustainability. Two types of empathy, cognitive empathy (i.e., understanding the affective experience of another) and emotional empathy (i.e., experiencing another's emotional state) were assessed. Job disengagement at 12 months was predicted by the interaction between animal euthanasia frequency in the past 12 months and emotional empathy in the past 6 or 12 months. Perceived resilience at 12 months was predicted by the interaction between animal euthanasia frequency in the past 12 months and emotional empathy a year prior. For these outcomes, the effects of performing animal euthanasia on career sustainability were moderated by emotional empathy. Higher levels of emotional empathy were associated with worse outcomes. Veterinarians may seek to understand the affective experience of the client or patient and provide compassionate care in a sustainable way. However, they should do so while avoiding the costs of emotional empathy. This work has implications for veterinarian training to support career sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".