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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".