Risk Factors Associated With Increased Ethically Challenging Situations Encountered by Veterinary Team Members During the COVID-19 Pandemic
Bibliographic record
Abstract
Ethically challenging situations (ECS) are commonly encountered in veterinary settings. The number of ECS encountered by some veterinary team members may increase during a crisis, such as the COVID-19 pandemic. This study aimed to determine the risk factors for experiencing an increase in the frequency of ECS in the months following the beginning of the COVID-19 pandemic, utilizing data from a global survey of veterinarians, veterinary nurses and animal health technicians collected from May to July 2020. In this study, descriptive analyses were performed to characterize veterinary team members who responded to the survey (n = 540). Binomial logistic regression analyses were performed to determine factors associated with an increase in ECS encountered since the beginning of the COVID-19 pandemic. Being a veterinary nurse or animal health technician, working with companion animals, working in the USA or Canada, and being not confident or underconfident in dealing with ECS in the workplace were factors associated with an increase in ECS encountered since the beginning of the COVID-19 pandemic. Results suggest a need to explore the ECS encountered by veterinary team members, particularly veterinary nurses and animal health technicians working in companion animal practice, in depth. Identification of risk factors may facilitate better preparation of veterinary team members for managing ECS, and minimizing the negative impact of ECS on the well-being of those who care for animals.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".