Impacts of the process and decision‐making around companion animal euthanasia on veterinary wellbeing
Bibliographic record
Abstract
A qualitative study using group and individual interviews involving 10 veterinary hospitals in Wellington County, Ontario, explored how the practices involved in euthanasia-related care impacts the wellbeing of veterinary professionals. Thematic analysis indicated two major outcomes: the goal and desire of veterinary professionals was to facilitate a 'good death' for the companion animal and navigating the euthanasia decision process was more challenging than the actual event of performing euthanasia. When successful in achieving a 'good death' and navigating euthanasia decisions, participants reported feeling that their own sense of wellbeing and the veterinary client's sense of wellbeing were improved. When unsuccessful, participants reported experiencing a reduced sense of wellbeing, reduced job satisfaction, increased emotional strain and feeling that the client was also detrimentally impacted. For many participants, navigating euthanasia decision-making consultations was seen as a greater challenge and a greater contributor to a reduced sense of wellbeing than the act of euthanasia itself. These findings suggest that there is a need for greater attention and support for veterinary professionals, particularly when navigating euthanasia decision-making consultations. Additional training and resources on navigating euthanasia consultations may assist in improving the wellbeing for veterinary professionals and the companion animals and owners under their care.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".