Veterinary technicians contribute to shared decision-making during companion animal veterinary appointments
Bibliographic record
Abstract
OBJECTIVE: To describe and compare veterinary professionals' use of shared decision-making during companion animal appointments. DESIGN: Multi-practice cross-sectional study. SAMPLE: A purposive sample of 4 companion animal veterinary clinics in a group practice in Texas. PROCEDURES: A convenience sample of veterinary appointments were recorded January to March 2018 and audio-recordings were analyzed using the Observer OPTION5 instrument to assess shared decision-making. Each decision was categorized by veterinary professional involvement. RESULTS: A total of 76/85 (89%) appointments included at least 1 decision between the client and veterinary professional(s), with a total of 129 shared decisions. Decisions that involved both a veterinary technician and veterinarian scored significantly higher for elements of shared decision-making (OPTION5 = 29.5 ± 8.4; n = 46), than veterinarian-only decisions (OPTION5 = 25.4 ± 11.50; P = .040; n = 63), and veterinary technician-only decisions (OPTION5 = 22.5 ± 7.15; P = .001; n = 20). Specific elements of shared decision-making that differed significantly based on veterinary professional involvement included educating the client about options (OPTION5 Item 3; P = .0041) and integrating the client's preference (OPTION5 Item 5; P = .0010). CLINICAL RELEVANCE: Findings suggest that clients are more involved in decision making related to their pet's health care when both the veterinary technician and veterinarian communicate with the client. Veterinary technicians' communication significantly enhanced client engagement in decision-making when working collaboratively with the veterinarian.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".