Clients prefer collaborative decision-making with veterinarians regardless of appointment type
Bibliographic record
Abstract
OBJECTIVE: To determine clients' preferences for veterinarians' communication during decision-making in relation to 3 clinical contexts: preventive care, general problem (eg, illness or injury), and urgent appointments. METHODS: A cross-sectional online questionnaire was distributed by use of snowball sampling to veterinary clients owning a pet. Demographic information was collected, and participants were then randomly assigned to a scenario reflecting one of the following clinical contexts (appointment types): preventive, general problem, or urgent. Discrete choice exercises were used to solicit client preferences for 5 aspects of veterinarians' communication approach to decision-making with clients in clinical contexts: amount of information, presentation of plan (options or a recommendation), communication of value, nature of client involvement, and communication of financial cost. Relative-preference scores were calculated using hierarchical bayesian modeling. RESULTS: Responses for 1,614 participants were included in the final analysis. The majority were female, lived in Canada, and were financially stable. Across clinical decision-making contexts, participants preferred to collaborate with the veterinarian, be offered options with a clear recommendation, be informed of present and future costs, and have benefits of any action regarding their pet's health explained to them. For the general-problem context, participants preferred veterinarians' communication to focus solely on concerns; in contrast, in preventive care and urgent contexts, participants preferred that communication include both normal findings and concerns. CONCLUSIONS: Irrespective of clinical decision-making context, participants preferred collaborative decision-making with a veterinarian. CLINICAL RELEVANCE: Veterinary professionals should consider, when possible, communication that supports a collaborative approach to decision-making with 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 | 0.008 | 0.038 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".