‘One Health’ promotion in a model city for dog-aggression policy: A qualitative inquiry in the City of Calgary
Notice bibliographique
Résumé
Background Dog-bite injuries remain a perennial problem, especially in pediatric emergency services. Nonetheless, few researchers have examined how local-level policies may contribute to primary prevention. We do so with qualitative research and an emphasis on implementation. This study highlights the potential benefit of coordination in Alberta between municipalities and emergency health services. Implementation This study mainly took place in the City of Calgary, which has earned a sterling reputation, in Canada and internationally, for the results of its animal-control policy in reducing dog-aggression incidents. We attribute part of this achievement to the high compliance of licensing in Calgary. The City estimates 80-90% of all dogs in Calgary have been licensed (by comparison, the City of Toronto estimates 35% compliance with mandatory licensing for dogs). The City of Calgary earmarks revenue from licensing for human-animal services, including public education, assessment of dogs’ behavior, and a state-of-the-art shelter oriented towards rehoming. Here, we frame the City of Calgary’s dog-aggression policy as a ‘One Health’ issue. This concept refers to human-animal-environment interdependencies as the basis for health. Whereas most One Health research has focused on preventing zoonotic infections or environmental toxins, our approach emphasizes health promotion, in which ‘caring for one’s self and others’ as the foundation for improving longevity and quality of life. Over the years, we have informed and learned from the City of Calgary’s implementation of its dog-aggression policy framework. Evaluation Methods Related research (Caffrey et al., 2019) has analyzed the City of Calgary’s administrative data on dog-bite incidents, statistically and spatially. Previously our team partnered with the Emergency Services Strategic Clinical Network on an analysis of emergency services utilization for dog-bite injuries across Alberta (Jelinski et al., 2016). We have also highlighted risks to occupational health and safety amongst officers who enforce dog-aggression policies, in Alberta and worldwide (Rault et al., 2018). In this presentation, we delve into how these officers act on municipal data when investigating dog-aggression incidents in the City of Calgary. Our main sources of information were semi-structured interviews and participant-observation. Results High compliance with dog-licensing bylaws in Calgary assists officers in efficiently locating dogs following a dog-aggression complaint. In turn, citizens lodge complaints because they view the City of Calgary’s human-animal services as effective and humane. References Caffrey, N., Rock, M., Schmidtz, O., Anderson, D., Parkinson, M., Checkley, S.L. Insights about the epidemiology of dog bites in a Canadian city using a dog aggression scale and administrative data. Animals, 9(6). doi: 10.3390/ani9060324. Jelinski, S.E., Phillips, C., Doehler, M., Rock, M. (May, 2016). The epidemiology of emergency department visits for dog-related injuries in Alberta. Canadian Journal of Emergency Medicine, 18(S1). doi: 10.1017/cem.2016.68 Rault, D., Nowicki, S., Adams, C., Rock, M. (2018). To protect animals, first we must protect law enforcement officers. Journal of Animal and Natural Resource Law, XIV, pp.1-33.
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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 | 0,011 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,030 | 0,021 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,004 | 0,008 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».