Comparison of food‐animal veterinarians’ and producers’ perceptions of producer‐centered communication following on‐farm interactions
Bibliographic record
Abstract
BACKGROUND: Human medicine has demonstrated that a patient-centered physician-patient relationship is more effective than the traditional physician-centered model. Objectives were to explore food-animal veterinarians' and producers' perceptions of producer-centered communication (VPPC and PPPC), during on-farm interactions and examine associated factors. METHODS: A cross-sectional sample of food-animal veterinarians and their clients were recruited in Ontario, Canada. Immediately following on-farm veterinarian-producer interactions, the producer and veterinarian independently completed a questionnaire assessing PPC. Symmetry of paired responses between veterinarians and producers was examined. Employing listwise deletion, independent mixed linear regression models were developed to determine factors associated with PPPC and VPPC, respectively. RESULTS: Two hundred and three paired veterinarian and producer survey responses were analysed. Significant asymmetry (p-value < 0.05) was observed, with veterinarians assessing PPC lower than producers. Based on data from 32 veterinarians and 159 producers, the only factor associated with PPPC was veterinarian burnout (PPPC decreased with burnout). Based on data from 32 veterinarians and 155 producers, factors positively associated with VPPC included veterinarian compassion satisfaction (VPPC increased with compassion satisfaction), length of interaction (VPPC increased with length of interaction) and producers identifying as female (VPPC higher with female producers). CONCLUSION: Producer's positive PPPC is encouraging, yet veterinarians should be aware that mental health parameters may impact producers' and their own perceptions of PPC. Further examining veterinarians' delivery of PPC is important for food-animal practice.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".