Evaluation of the impacts of a one-time subsidized preventive veterinary clinic intervention in two First Nations communities in Alberta
Bibliographic record
Abstract
Remote communities in Northern Canada face difficulties accessing veterinary services. Often this lack of access to veterinary services impacts the communities’ health and safety as a whole and highlights animal welfare concerns. These concerns are more noticeable when the free-roaming dog population threatens people’s safety and health. These communities request assistance from charitable organizations, such as the Canadian Animal Task Force (CATF) to help with dog population management and address the lack of access to veterinary services. Overall, there has been a lack of scientific evaluation regarding the impacts of subsidized preventive veterinary services in Canada and specifically in Indigenous communities. The main objective of this thesis was to evaluate the impacts of a one-time high-volume preventive veterinary clinic intervention in two First Nations communities over 1 year. This thesis is based on the secondary use of the data collected by CATF from the townsite of two specific communities at three time points: pre-clinic, 6 months post-clinic, and 12 months post-clinic. This evaluation revealed that i) the welfare and health of observed dogs improved in both communities; and ii) respondents’ perspectives around negative human-dog interactions improved in one of the study communities but not the other. Although the findings of this study were limited due to various external factors (e.g., events that occurred in the first study community, low response rate), it is hoped that lessons learned will enlighten future evaluation programs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".