ETHICAL DILEMMAS AND MORAL DISTRESS IN COMPANION ANIMAL VETERINARY MEDICINE: MENTAL HEALTH IMPLICATIONS
Bibliographic record
Abstract
Mental health concerns are a significant problem in the veterinary community, and studies suggest that veterinarians working in Western countries are at higher risk of suicide compared to the general population and other healthcare professionals (Bartram et al., 2009; Bartram & Baldwin, 2010; Platt et al., 2012). Additionally, veterinary medicine is an occupation inundated with complex ethical and moral dilemmas. However, current studies are limited in their ability to contextualize the risk and contributing factors fully; therefore, they offer limited insight into effective preventative and support strategies. Using Interpretive Phenomenological Analysis (IPA), eight companion animal veterinarians were interviewed about their experiences of moral distress and ethical dilemmas in veterinary practice. The research question guiding this inquiry was: What are the experiences of moral distress and ethical dilemmas among companion animal veterinarians practicing in Canada? Five key themes emerged. First, veterinarians identified the most distressing aspects of their work. Second, they identified the negative emotional repercussions of their work. Third, while some identified coping strategies to help mitigate the stress associated with their work, they also noted the challenges of implementing these strategies within the current veterinary practice structure. Fourth, veterinarians identified their motivations for continuing to practice veterinary medicine despite the mental health challenges it poses. Fifth, participants made various recommendations for change within veterinary medicine and the educational curriculum to help improve mental health outcomes among veterinarians. The themes identified in this study relate to the fee-for-service structure of veterinary medicine and how finances contribute to ethical dilemmas, moral distress, abusive interactions from clients, adversely impact animal welfare, and threaten the human-animal bond. In the existing model of care, veterinarians’ mental health is adversely impacted, and socioeconomically disadvantaged pet caregivers risk losing meaningful relationships with their animals. This study supports the need for a change in the structure of veterinary medicine and increased community funding to improve access to veterinary care. This research further underscores the importance of adopting an approach to healthcare which recognizes the interdependence of human and non-human health and well-being.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".