‘One Health’ promotion in a model city for dog-aggression policy: A qualitative inquiry in the City of Calgary
Bibliographic record
Abstract
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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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".