Mapping veterinary care in Canada: An index of care accessibility.
Bibliographic record
Abstract
Background: Access to veterinary care has been identified as the largest animal welfare issue in contemporary society. Access to veterinary care is complicated by several factors, including the cost of care, potential language differences between providers and clients, the number of care providers, and distance to a care provider. Each of these factors alone can impact an individual's ability to seek adequate veterinary medical care for their companion animal, with an additional burden when multiple factors are present. Procedure: A veterinary care accessibility score (VCAS) was created, consisting of key variables for Canada, that measured these factors and scored them in relation to the rest of the country at the census division level. Results: In this study, nearly 2 million households in Quebec and 700 000 in Ontario were in the lowest VCAS ranking. Further, nearly 75% of households in New Brunswick were in low-access census divisions. The ratios of care providers to the estimated numbers of pet-owning households and households were also derived. An estimated veterinary clinic employee shortage was calculated at a minimum of 6803 to simply bring every census division up to a weighted mean, although the actual shortage is likely higher. Conclusion: This research could be used by policymakers, funders, and the animal welfare community to prioritize investment and design targeted solutions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".