Analysis of solicitation of client concerns in companion animal practice
Bibliographic record
Abstract
OBJECTIVE: To examine veterinarian solicitation of client concerns in companion animal practice. DESIGN: Cross-sectional descriptive study. SAMPLE-20 veterinarians in companion animal practice in Eastern Ontario and 334 clients and their pets. PROCEDURES: Beginning segments of 334 appointments were coded for a veterinarian solicitation (open- or closed-ended question) used to elicit client concerns. Appointments including a solicitation were analyzed for completion of the client's response and its length. The association between veterinarian solicitations at the beginning and concerns arising at the closure of the interview was examined. RESULTS: 123 (37%) of the coded appointments contained a veterinarian solicitation, of which 93 (76%) were open-ended and 30 (24%) were closed-ended solicitations. Client responses to a solicitation were interrupted in 68 of 123 (55%) appointments. Main reasons for incomplete client responses were veterinarian interruptions in the form of closed-ended questioning (39/68) and noninterrogative statements (18/68). Median length of time clients spoke before interruption was 11 seconds (range, 1 to 139 seconds; mean, 15.3 seconds; SD, 12.1 seconds). The odds of a new concern arising during the closing segment of an appointment were 4 times as great when the appointment did not contain a veterinarian solicitation at the beginning of the interview. CONCLUSIONS AND CLINICAL RELEVANCE: Not soliciting client concerns at the beginning of an interview increased the odds of a concern arising during the final moments of the interaction. This required the veterinarian to choose among extending the appointment to address the concern, ignoring the concern at a possible cost to client satisfaction, or deferring the concern to another visit.
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".