Comparison of online, hands-on, and a combined approach for teaching cautery disbudding technique to dairy producers
Notice bibliographique
Résumé
The use of pain control for disbudding and dehorning is important from both an animal and industry perspective. Best practices include the use of local anesthetic, commonly given as a cornual nerve block (CNB), and a nonsteroidal anti-inflammatory drug. The proportion is decreasing, but many dairy producers do not use local anesthesia, perhaps in part due to lack of knowledge of the CNB technique. Although this skill is typically learned in person from a veterinarian, alternative methods may be useful. The objective of this trial was to determine if there were differences in the efficacy of online training (n = 23), hands-on training (n = 20), and a combined approach (n = 23) for teaching producers to successfully administer a CNB and disbud a calf. The primary outcome was block efficacy, defined as a lack of established pain behaviors during iron application. Secondary outcomes were background knowledge (assessed by a written quiz), CNB and disbudding technique (evaluated by rubric scoring), time taken, and self-confidence before and after evaluation. Associations between training group and outcome were assessed with logistic regression, ordered logistic regression, and Cox-proportional hazard models, with a random effect for workshop. Block efficacy was not different between training groups, with 91% successful in both combined and online groups, and 75% in the hands-on trained group. Online learners had poorer technical scores than hands-on trainees. The combined group was not different from hands-on. Time to block completion tended to be longer for the online group (62 ± 11 s), whereas time to disbudding completion was not different between hands-on (41 ± 5 s) or combined trainees (41 ± 5 s). The combined group had the highest pre-evaluation confidence score, and remained higher after evaluation than online but was not different than hands-on. Although we saw some statistical differences between groups, absolute differences were small and block efficacy was similar. This suggests online training can be a useful tool for motivated producers who lack access to hands-on training.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,003 | 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 ».