Access to veterinary care in Canada: a cross-sectional survey of animal healthcare organizations and interventions
Bibliographic record
Abstract
Introduction: Many Canadians struggle to access healthcare for their animals, but little data is available from the Canadian context on how barriers to care are being addressed, and with what effects. Methods: The aim of this research was to characterize service providing organizations, barrier mitigation tools, community partnerships, and evaluation metrics used by organizations attempting to increase access to animal healthcare in Canada. In this study, we conducted online data mining and a cross-sectional, mixed-methods organizational survey. Results: = 97) were received from non-profit organizations (52%), for-profit clinics (38%), and several municipal or governmental services (4%) and educational institutes (5%). Commonly reported tools included no cost or low-cost services, pop-up clinics and providing items to assist with pet transportation, with many other tools (payment plans without a credit check, services in multiple languages, availability of assistive technology) being employed by fewer than 20% of responding organizations. Only 38% of organizations used at least one tool from each of the four categories of barriers. Community involvement in programs ranged from simply accessing the service when it was available (outreach) to giving occasional feedback on their experiences (consulting), being employed or volunteering in program provision (collaborating), and community leadership partnering on initiatives (sharing leadership). Program evaluation most often involved quantitative measures of service usage with fewer organizations formally soliciting feedback from the community or looking at long-term health impacts. Discussion: Responses demonstrate that organizations employ a wide range of tools to mitigate access to veterinary care barriers primarily along financial and geographical lines, and to a lesser extent with tools targeting cultural or disability-related barriers highlighting the importance of building capacity around addressing multiple intersecting barriers. Study findings provide a baseline characterization of current efforts by Canadian organizations to mitigate barriers to accessing animal healthcare.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".