Perceived barriers of dairy producers to the adoption of selective antimicrobial therapies for nonsevere clinical mastitis and at dry-off in dairy cattle: A focus group study in Ontario, Canada
Notice bibliographique
Résumé
Prudent antimicrobial use (AMU) in the dairy industry is crucial as it affects animal health and welfare and could help to slow the development of antimicrobial resistance. There is a need to adopt selective AMU. However, the barriers to adoption of selective antimicrobial use for the management of mastitis and dry-off are not adequately described. The objective of this study was to understand the barriers that dairy farmers in Ontario faced in the adoption of selective antimicrobial therapy for nonsevere clinical mastitis and at dry-off in dairy cattle. Six focus groups were held in 2 regions of Ontario (southwestern [n = 3] and eastern [n = 3]) involving 35 dairy farmers. Three themes were identified from the transcribed discussions: (1) experiences with selective antimicrobial mastitis and dry-off therapies, (2) risk tolerance for selective antimicrobial mastitis and dry-off therapies, and (3) factors influencing the adoption of selective antimicrobial mastitis and dry-off therapies. Participants viewed the decision to adopt selective antimicrobial mastitis and dry-off therapies to be the responsibility of the dairy producer. They described the use of bacterial diagnostics for selective treatment of nonsevere clinical mastitis as frustrating because results were not delivered in time to inform treatment. Some participants were not receptive to selective dry-off therapy because they perceived it placed their cows at high risk for mastitis during the next lactation. Participants who used selective dry-off therapy often mitigated these initial concerns by beginning this strategy with a small group of low-production animals. Individual cow data from automatic milking systems and record keeping were viewed as instrumental to the success of selective AMU but human elements (e.g., visual assessment of animals) continued to be used in the decision-making process. Some participants described cognitive dissonance and a reluctance to change when selective AMU to manage mastitis appeared to be in conflict with previously recommended blanket treatment practices. Our results suggest that cognitive dissonance experienced by participants may be mitigated by information from trusted sources, such as veterinarians. Additionally, peer-to-peer learning opportunities (e.g., dairy producers learning from colleagues' experiences and reflecting on their own current practices) could be used to facilitate evaluation of whether adoption of selective AMU aligns with their management approach for clinical mastitis. Therefore, until rapidity of mastitis diagnostics and communication of results improves for selective lactation therapy, and the perceived mastitis risk related to selective dry-off therapy is addressed, challenges will continue for the adoption of best management practices for selective antimicrobial therapy for nonsevere clinical mastitis and at dry-off in dairy cattle.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,004 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».