Conversation Analysis of Veterinarians' Proposals for Long-Term Dietary Change in Companion Animal Practice in Ontario, Canada
Bibliographic record
Abstract
Nutritional changes recommended by veterinarians to clients can have a major role in animal-patient health. Although there is literature on best practices that can inform veterinary communication training, little is known specifically about how veterinarians communicate their recommendations to clients in real-life interactions. This study used the qualitative research method of conversation analysis to investigate the form and content of veterinarian-initiated proposals for long-term dietary change in canine and feline patients to further inform veterinary communication training. We analyzed the characteristics and design of veterinarian-initiated proposals for long-term nutritional modification as well as the appointment phases during which they occurred, in a subsample of 42 videotaped segments drawn from 35 companion animal appointments in eastern Ontario, Canada. Analyses indicated that veterinarians initiated proposals at various points during the consultations rather than as a predictable part of treatment planning at the end. While some proposals were worded strongly (e.g., "She should be on…"), most proposals avoided the presumption that dietary change would inevitably occur. Such proposals described dietary items as options (e.g., "There are also special diets…") or used mitigating language (e.g., "you may want to try…"). These findings seem to reflect delicate veterinarian-client dynamics associated with dietary advice-giving in veterinary medicine that can impact adherence and limit shared decision-making. Our analyses offer guidance for communication training in veterinary education related to dietary treatment decision-making.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".