Dairy farmers' expectations and receptivity regarding animal welfare advice: A focus group study
Notice bibliographique
Résumé
Discussion and incorporation of best practices for animal welfare have been increasing in research, and in commercial operations, including through welfare assessment initiatives. The aim of this study was to explore dairy farmers' perceptions about being approached and receiving advice about animal welfare (i.e., lameness, hock injuries, and disbudding practices). It is useful for dairy consultants, researchers, or animal welfare assessment programs to gain an in-depth understanding of farmers' expectations when broaching the subject of animal welfare issues to facilitate communication about what can be perceived as a sensitive topic. We collected qualitative data using a focus group methodology. Five focus groups of farmers (n = 36 in total), took place in Ontario, Canada. Discussions were audio-recorded and transcribed verbatim. Participant age ranged from 21 to 80 yr (median = 47). Represented herd size ranged from 25 to 550 milking cows (median = 75). Farm type included free stall (n = 14) and tie stall (n = 22). Rigor was incorporated by using systematic thematic analysis: transcripts were coded line by line, and codes were categorized and then expanded and collapsed into themes, which were further refined to reflect farmer perceptions in a thematic map. Thematic analysis of the focus group discussions suggested 6 major themes related to farmers' receptivity to and expectations of animal welfare advice. Themes 1 to 4 provided insights into what farmers expected from those who were broaching topics: (1) an established relationship with the farmer; (2) expertise in dairy care/welfare; (3) prevention of "barn blindness"; and (4) provision of animal care services before and after welfare issues are broached. Theme 5 helped determine how welfare topics should be broached: the communication approach. Theme 6 identified who farmers feel should broach animal welfare topics on farm. Focus group discussions also provided insights into potential disconnects between farmer and veterinary expectations about animal welfare issues during herd health visits. Those who have established relationships with farmers are better received and are expected to broach welfare issues, especially if they are perceived to be an expert in animal care and welfare, and if they communicate the issue tactfully and work with the farmer to establish a plan of action.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».