Bibliographic record
Abstract
Views: Commentary 263 I veterinary practice, clients often must make treatment choices for their animals, and the decisions they make can determine the outcome of treatment, in that owners can refuse to consent to a treatment option, choose not to follow treatment recommendations, or elect not to pursue any treatment at all. Clients do not make these decisions alone. Veterinarians work at the interface between owners and their animals and have an important role in the decisions owners make about their animals. They inform their clients about examination and test results, provide diagnoses, recommend possible treatment options, and advise on likely prognoses. They have an expertise, trustworthiness, and authority that make clients value their opinions about important and emotional issues. Veterinarians’ roles therefore afford them myriad opportunities to influence their clients’ choices. But is exercising such influence legitimate? On the one hand, it could be argued that any influence that benefits patients is acceptable, if not mandatory. But, the doctrine of informed consent suggests that influence is generally unacceptable. The Royal College of Veterinary Surgeons, in its Guide to Professional Conduct, states that veterinarians should “respect [clients’] views” and “recognize that the client has freedom of choice.” Similarly, the AVMA, in its Principles of Veterinary Medical Ethics, states that veterinarians “should not engage in fraud, misrepresentation, or deceit.” On the other hand, influence could be argued as intrinsically wrong. However, some prominent American ethicists have suggested that persuasion might be acceptable in some cases, although without suggesting criteria for determining which cases those are. The Royal College of Veterinary Surgeons also adds that “veterinarians must ... give due consideration to the client’s concerns and wishes where these do not conflict with the patient’s welfare.” How can a practitioner balance these extreme views? We believe that an approach based on reasonableness can provide a framework for practitioners to decide how much influence is legitimate. Drawing on US, Canadian, and United Kingdom legal precedent and ethical commentary, the present commentary examines possible forms of and reasons for influence and then The ethics of influencing clients
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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 | 0.043 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.050 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.151 | 0.119 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".