Pet owners’ and veterinarians’ perceptions of information exchange and clinical decision-making in companion animal practice
Bibliographic record
Abstract
One of the most complex aspects of the veterinarian-client-patient interaction is the clinical decision-making process. Research suggests that the approach to communication used by veterinarians can impact veterinary clients' involvement in the decision-making process and their ultimate satisfaction. Using different approaches to the decision-making process may affect how information is exchanged and consequently how decisions are made. The objective of this study was to determine pet owners' expectations with respect to information exchange and decision-making during veterinarian-client-patient interactions and to compare veterinarians' perceptions of those expectations and the challenges they face in meeting them. Five pet owner focus groups (27 owners) and three veterinarian focus groups (24 veterinarians) were conducted with standardized open-ended questions and follow-up probes. Thematic analysis of the transcribed data was conducted to identify trends and patterns that emerged during the focus groups. Three pet owner-based themes were identified: 1) understanding the client; 2) providing information suitable for the client; and 3) decision-making. In addition, three barriers for veterinarians affecting information exchange and decision-making were identified: 1) time constraints; 2) involvement of multiple clients; and 3) language barriers. Results suggest that pet owners expect to be supported by their veterinarian to make informed decisions by understanding the client's current knowledge, tailoring information and educating clients about their options. Breakdowns in the information exchange process can impact pet owners' perceptions of veterinarians' motivations. Pet owners' emphasis on partnership suggests that a collaborative approach between veterinarians and clients may improve client satisfaction.
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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".