Ethically challenging situations encountered by veterinary team members
Bibliographic record
Abstract
Veterinary team members (VTMs) commonly encounter ethically challenging situations (ECS). ECS can lead to moral distress & impact the safety & welfare of patients. Literature searches (1990−2020) & a focused review on advanced veterinary care (AVC) identified key ECS. A global survey of VTMs during the early COVID-19 pandemic explored frequency, stressfulness & types of ECS encountered by VTMs. Frequency increased for almost half of VTMs during the pandemic. ECS encountered by VTMs, resources used to resolve ECS, & barriers to resolution are discussed. Risk factors for experiencing increased ECS during the pandemic included being a veterinary nurse or animal health technician, working with companion animals, working in USA/Canada & having low confidence dealing with ECS in the workplace. Qualitative analysis identified key factors that may lead to or exacerbate ECS during the pandemic: communication challenges & low or no-contact euthanasia. Strategies to prevent or mitigate ECS are recommended. Access to resources (e.g. technology to facilitate telemedicine, protocols to facilitate low-contact euthanasia) are needed to prevent/mitigate ECS impacts. Ethics rounds (ER), used in medical settings, was trialed with VTMs, who completed the Euro-MCD 2.0 pre & post. The Euro-MCD evaluates outcomes of ethics rounds across domains of moral competence, moral teamwork & moral action. VTMs improved in the domains of moral competence & moral teamwork after 1 session of ER. ER has potential to improve the ability of VTMs to identify & navigate ECS & to mitigate moral distress. Recommendations: veterinary empirical ethics research to include perspectives of non-veterinarian VTMs & clients, develop a validated measurement of veterinary team member moral distress, challenge the triad of veterinary stakeholders, further evaluate & develop CESS, conduct regular surveillance of ECS & to prepare VTMs, clients, animals & other stakeholders for ECS occurring in emergencies.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".