Trends in companion animal access to veterinary care in Canada, 2007 to 2020.
Bibliographic record
Abstract
Objective: Assess trends in access to veterinary care for companion animals in Canada. Procedure: Analysis and integration of available data, 2007 to 2020. Results: Cumulative growth in the Canadian veterinary workforce was 38%, and 49% for companion animal veterinarians. Clients per companion animal veterinarian decreased 30% from 2008 to 2020. Absolute client numbers increased 1.3%, compared to pet population growth of 17%. Medicalized pets (those that had received veterinary care in the past year) increased 25%, from 9.02 million in 2007 to 11.24 million in 2020. Non-medicalized pets increased 1.8%, from 4.48 million to 4.56 million. In 2007, 33% of pets were non-medicalized, compared to 29% (15% of dogs and 42% of cats) in 2020. There was a cumulative increase of 31% for total non-medicalized dogs, and a change of -5.6% for cats. Gross and net revenues per client increased by 99 and 112%, respectively, compared to cumulative inflation of 21%. Conclusion and clinical relevance: The analysis identified a large cohort of pets that had not received veterinary care each year. The trends were fewer clients per veterinarian, each paying higher veterinary costs, and suggested a relative, rather than absolute, veterinary capacity shortage overall. Accessible care-provision models must be encouraged, regulated for, and allowed to flourish alongside traditional models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".