Barriers and opportunities for improving dog bite prevention and dog management practices in northern Indigenous communities
Bibliographic record
Abstract
Globally, people living in northern Indigenous communities are at higher risk of dog bites than the rest of the population living in North America, with annual incidence ranging from 0.61 to 59.6/10,000 inhabitants. Considering that rabies is endemic in wild canid populations in certain regions of the Arctic, the prevention of dog bites and the management of dog populations are of crucial importance for public health in these contexts. Most northern communities lack access to veterinary services, mainly due to their remote geographical location and to limited financial resources. Currently, northern Indigenous communities are using different approaches and strategies to prevent dog bites and manage dog populations, but the effectiveness of these approaches sometimes lacks evidence, and their low acceptability may affect their implementation. This study aims to describe (1) the current access and uses of veterinary services, and (2) the perceived barriers and opportunities related to dog population management practices currently implemented, or that could be implemented, in a Naskapi community and an Innu community located in northern Quebec (Canada). Quantitative data were collected through a survey to inhabitants on veterinary services (n = 122). Qualitative data were collected using individual interviews to inhabitants and health professionals to describe how dog population management measures were perceived, and to identify barriers and opportunities related to their implementation (n = 37). Descriptive and inferential analysis (quantitative data) and thematic analysis (qualitative data) were performed. Results show that the two main measures implemented at the time of the study – dog culling and short-duration veterinary clinics – were not perceived as fully acceptable and sustainable. Reinforcing access to veterinary services and other dog-related services, such as shelters and training programs on dogs, was identified as a need to improve dog bites prevention and dog population management in remote Indigenous communities. The implementation of animal health measures should be decided by concerned Indigenous communities to follow decolonial practices. It includes ensuring informed consent of dog owners, improving communication before, during and after interventions, separating veterinary services from rehoming and, most importantly giving back to Indigenous communities the complete leadership over animal health in their communities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".