Exploration et comparaison des attentes d'éleveurs bovins laitiers et allaitants vis-à-vis de la communication interpersonnelle de leur vétérinaire en consultation
Bibliographic record
Abstract
In veterinary medicine as well as in human medicine, interpersonal communication during consultation is widely recognized as an essential skill for establishing a good relationship with the client. It allows for better client and practitioner satisfaction, and is increasingly taught in veterinary schools around the world. In rural veterinary medicine, communication is recognized as important but the communication practices to be used in French cattle farms are poorly documented. Through semi- directed interviews with nine French cattle farmers, this work explores farmers' expectations in terms of communication with their veterinarians. From this initial sample, it appears that breeders want an equal partnership with their veterinarian, and a relationship based on mutual respect, detailed explanations and adaptation. The skills developed in the Calgary-Cambridge guide such as “offering partnership” and “using easily understood language” are essential to developing this relationship. The farmers interviewed were mostly satisfied with their relationship with their veterinarian and their communication skills. Furthermore, few differences in expectations between dairy farmers and beef farmers have been highlighted. This qualitative study offers a first exploration of the expectations of French cattle farmers in terms of communication and could be extended to a quantitative study to confirm or refute those results.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".