Barriers and Lack of Access to Veterinary Care in Canada 2022
Bibliographic record
Abstract
Introduction: Despite concerns about access to veterinary care in Canada, there are no previously published national survey data. The study aimed to estimate the prevalence of barriers to veterinary care faced by Canadian dog and cat owners, and to identify associated factors. Methods: This was a national online survey conducted in mid-2022, the third year of the COVID-19 pandemic. It was nationally representative of English and French-speaking Canadian adults as regards region, age, and sex. Results: Eighteen per cent of the respondents (440/2,500) could not access wanted or needed preventative veterinary care in the past 12 months; 12% (305/2,500) could not access sick care; and 8% (195/2,500) could not access emergency care. The most frequent barriers were the inability to afford care (preventative – 124/440, 28% of those who were unable to access care; sick – 75/305, 25%; emergency – 34/195, 17%); and the inability to obtain an appointment (preventative – 95/440, 22% of those who were unable to access care; sick – 80/305, 26%; emergency – 47/195, 24%). Twenty-one per cent (522/2,500) could not access other pet needs, most frequently pet food (43% of those who lacked access); grooming (34%); and training (28%). Recent immigrants (<5 years) and young people (18–34 years old) were more likely to report barriers. Affordability and appointment availability were the two most frequently occurring barriers. Conclusions: This survey identified a large number of pet owners who faced barriers to veterinary care. There is a need for industry leaders, educators and regulators to help support initiatives to expand access to care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".