Decision: Implementation evaluation of a pilot program addressing issues at the human-dog interface in a northern indigenous community — R0/PR4
Bibliographic record
Abstract
In 2020, the Kuujjuaq Dog Project (KDP) was operationalized in the Northern Village of Kuujjuaq (Québec, Canada) to mitigate issues at the human–dog interface. Differing from previous interventions in its EcoHealth approach, it provided veterinary services, educational components (school workshops and Facebook posts) and strengthened local dog control measures. In 2022, an implementation evaluation assessed its quality of its delivery, differentiation, adaptations and the community’s responsiveness. The study followed key principles of the One Health approach and a mixed-methods design, combining a survey of 74 participants and individual interviews with 10 key stakeholders and 25 other community members. Analysis confirmed the relevance of the KDP, highlighting its innovative nature compared to previous dog-related interventions in northern Québec. Awareness of and exposure to the KDP’s components varied considerably between veterinary services (89%), Facebook posts (55%) and school workshops (27%). Global exposure to both the veterinary services and educational components was lower among Inuit, men and non-dog owners (not statistically significant). Barriers and facilitators to implementation included long-term engagement of stakeholders and building meaningful partnerships with local stakeholders. Beside supporting the future evolution of the KDP, this study fills knowledge gaps on how to successfully implement integrated, participatory interventions in northern Indigenous 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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".