Veterinary Student Opinions Regarding Ethical Dilemmas Encountered by Veterinarians and the Benefits of Ethics Instruction
Bibliographic record
Abstract
= 284) from four US schools were surveyed regarding their opinions on ethical dilemmas encountered by veterinarians and the benefits of ethics instruction. The majority of respondents had encountered all clinical scenarios that may be associated with ethical dilemmas that were provided. The most common ethical dilemma experienced was compromise of patient care because of financial limitations. Students with at least 12 months of experience were more likely to believe that practitioners encounter ethical dilemmas regularly. Although 92% of 271 respondents indicated that veterinarians should prioritize patient interests when the interests of clients and patients conflict, 84% of respondents reported that veterinarians most often prioritize client interests. Most (78%) respondents indicated having received training in ethical theories and approaches to address ethical dilemmas. The majority of respondents agreed that they feel better prepared to identify (80%) and address (55%) ethical dilemmas as a result of their ethics training. Most respondents (81%) identified experiencing moral stress in relation to how animals were treated. Only 46% of respondents reported receiving training in tools for coping with moral stress. Most of these respondents (54%) agreed that such training would be effective in helping to manage moral stress. Results suggested that educators should prepare students for the contrast in advocacy preferences they are apt to encounter when they enter practice. It is recommended that ethics training and tools for coping with moral stress be core components of the veterinary curriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".