Effect of veterinarian-client-patient interactions on client adherence to dentistry and surgery recommendations in companion-animal practice
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between veterinarian-client-patient interactions and client adherence to dental and surgery recommendations and to test the a priori hypotheses that appointment-specific client satisfaction and relationship-centered care are positively associated with client adherence. DESIGN: Cross-sectional study. SAMPLE: A subsample of 19 companion-animal veterinarians and 83 clients from a larger observational study consisting of 20 randomly recruited veterinarians and a convenience sample of 350 clients from eastern Ontario. PROCEDURES: Videotaped veterinarian-client-patient interactions containing a dentistry recommendation, surgery recommendation, or both were selected for inclusion from the larger sample of interactions coded with the Roter interaction analysis system. Client adherence was measured by evaluating each patient's medical record approximately 6 months after the videotaped interaction. The clarity of the recommendation, appointment-specific client-satisfaction score, and relationship-centered care score were compared between adhering and nonadhering clients. RESULTS: Among the 83 veterinarian-client-patient interactions, 25 (30%) clients adhered to a dentistry recommendation, surgery recommendation, or both. The odds for adherence were 7 times as great for clients who received a clear recommendation, compared with clients who received an ambiguous recommendation from their veterinarian. Moreover, adhering clients were significantly more satisfied as measured after the interview. Interactions resulting in client adherence also had higher scores for relationship-centered care than did interactions leading to nonadherence. CONCLUSIONS AND CLINICAL RELEVANCE: Veterinarian use of a relationship-centered care approach, characterized as a collaborative partnership between a veterinarian and a client with provision of clear recommendations and effective communication of the rationale for the recommendations, has positive implications for client adherence.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".