Factors associated with food‐animal producer visit‐specific satisfaction following on‐farm interaction with a veterinarian
Bibliographic record
Abstract
BACKGROUND: Effective communication has been found to have positive associations with client satisfaction, client adherence and veterinarian satisfaction in companion animal medicine. Better understanding the role of communication on these outcomes, specifically producer satisfaction, is likely to benefit food-animal medicine. Objectives were to identify factors associated with producer visit-specific satisfaction following on-farm veterinarian-producer interactions and evaluate the validity of the Client Satisfaction Questionnaire for use in food-animal medicine. METHODS: A cross-sectional sample of practicing food-animal veterinarians and their producer clients were recruited in Ontario, Canada. Immediately following the veterinarian-producer interaction, producers completed a questionnaire that assessed their visit-specific satisfaction and their perception of the producer-centeredness of the interaction. Veterinarians completed a questionnaire that assessed their perception of the interaction's producer-centeredness. A linear regression model was developed to identify factors associated with producer visit-specific satisfaction. RESULTS: Forty-one veterinarians and 207 producers participated. Factors associated with producer visit-specific satisfaction included producer age (satisfaction decreased with age), producer gender (males less satisfied) and producer perception of producer-centeredness score (satisfaction increased with producer perception of producer-centeredness score). CONCLUSION: Findings raise awareness of the importance of a producer's perception of producer-centeredness in food-animal practice and encourage its use by food-animal veterinarians in working towards positive outcomes.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".