Uptake, Impact, and Lessons Learned from the Provision of 11 Years of Annual Subsidized Veterinary Services in the Sahtu Settlement Area, Northwest Territories, Canada
Bibliographic record
Abstract
Veterinary services are unavailable in many communities, which contributes to issues with human and animal health and well-being. Providing veterinary services in an evidence-based manner is important, but programs are rarely evaluated. The objectives of this research were to scope the literature to determine how subsidized veterinary services are evaluated in terms of impacts on animal and human health, followed by an evaluation of a decade-long program in the Sahtu Settlement Area of the Northwest Territories to understand the uptake and impact of annual services. Using methods commonly found in literature, a door-to-door survey, a dog census in each community, and a chart review of dog medical records from clinics in 2008-2018, were completed to evaluate community perspectives, the uptake of services, and changes in 7 dog population health and welfare measures over time. The number of owners and dogs attending clinics increased over time, as did the sterilization, vaccination and deworming of dogs, and dog body condition and age. Community differences, however, were evident in program reach, service uptake, dog husbandry practices, and community concerns about dogs. Results from this evaluation will improve future clinics and may guide programs in other underserved areas.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".