Effect of question design on dietary information solicited during veterinarian-client interactions in companion animal practice in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To establish the types of initial questions used by veterinarians in companion animal practice to solicit nutritional history information from owners of dogs and cats, the dietary information elicited, and the relationship between initial question-answer sequences and later nutrition-related questions. DESIGN: Cross-sectional qualitative conversation analytic study. SAMPLE: 98 appointments featuring 15 veterinarians drawn from an observational study of 284 videotaped veterinarian-client-patient visits involving 17 veterinarians in companion animal practices in eastern Ontario, Canada. PROCEDURES: Veterinarian and client talk related to patient nutrition was identified and transcribed; conversation analysis was then used to examine the orderly design and details of talk within and across turns. Nutrition-related discussions occurred in 172 visits, 98 of which contained veterinarian-initiated question-answer sequences about patient nutritional history (99 sequences in total, with 2 sequences in 1 visit). RESULTS: The predominant question format used by veterinarians was a what-prefaced question asking about the current content of the patient's diet (75/99). Overall, 63 appointments involved a single what-prefaced question in the first turn of nutrition talk by the veterinarian (64 sequences in total). Dietary information in client responses was typically restricted to the brand name, the subtype (eg, kitten), or the brand name and subtype of a single food item. When additional diet questions were subsequently posed, they typically sought only clarification about the food item previously mentioned by the client. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that question design can influence the accuracy and completeness of a nutritional history. These findings can potentially provide important evidence-based guidance for communication training in nutritional assessment techniques.
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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 | 0.052 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".