Prevalence and nature of cost discussions during clinical appointments in companion animal practice
Bibliographic record
Abstract
OBJECTIVE: To determine prevalence and nature of cost discussions between veterinarians and pet owners during clinical appointments in companion animal practice. DESIGN: Cross-sectional descriptive study. SAMPLE POPULATION: 20 veterinarians in companion animal practice in eastern Ontario and 350 clients and their pets. PROCEDURES: 200 veterinarian-client-patient interactions were randomly selected from all videotaped interactions and analyzed with the Roter interaction analysis system. Additional proficiency codes and blocking functions were developed to capture the prevalence, nature, and context of cost discussions. RESULTS: 58 of the 200 (29%) appointments that were analyzed included a discussion of cost. During 38 of these 58 (66%) appointments, the discussion involved costs associated with the veterinarian's time or with services provided by the veterinarian. Overall, reference to a written estimate was made during only 28 of the 200 (14%) appointments. Cost discussions were most common during appointments in which a decision related to diagnostic testing or dentistry was made. Appointments were significantly longer when a cost discussion was included than when it was not. CONCLUSIONS AND CLINICAL RELEVANCE: Results of the present study suggested that discussions related to cost were relatively uncommon during clinical appointments in companion animal practice and that written estimates were infrequently used to aid these discussions. When discussions of cost did occur, veterinarians appeared to focus on explaining costs in terms of the veterinarian's time or services provided by the veterinarian, rather than on the medical information that could be obtained or the benefits to the future health or function of the pet.
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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 | 0.003 | 0.036 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".