Do Rural Patients in Canada Underutilize Preventive Care for Myocardial Infarction?
Bibliographic record
Abstract
PURPOSE: The objective of this study was to explore Canadian rural-urban differences in the use of primary and secondary preventive diagnostic services for acute myocardial infarction (AMI)-a condition that is amenable to primary health care prevention efforts. METHODS: We examined primary and secondary preventive care services utilized 2 years prior to a patient's first AMI in a cohort of 30,491 patients in Ontario, Canada, from 2010 to 2012. Using logistic regression, rural-urban differences in lipid testing, glucose testing, stress testing, electrocardiograms, and echocardiograms in middle-age and senior patients were examined. FINDINGS: The odds of rural patients receiving care for primary preventive screening were more than 50% lower than the odds for urban patients, with rural seniors being the most affected. Lipid testing: middle-age OR 0.519 (95% CI, 0.469-0.574), senior OR 0.422 (0.386-0.460); glucose testing: middle-age OR 0.471 (0.426-0.521), senior OR 0.359 (0.328-0.394). The odds of rural patients receiving secondary preventive diagnostic care were also lower than the odds for urban patients, but differences between the age groups were not as apparent. Stress testing: middle-age OR 0.745 (95%CI, 0.642-0.866), senior OR 0.726 (0.643-0.820); electrocardiogram: middle-age OR 0.815 (0.737-0.901), senior OR 0.724 (0.659-0.795); echocardiogram: middle-age OR 0.755 (0.655-0.869), senior OR 0.746 (0.681-0.818). CONCLUSIONS: Study results support ongoing concerns related to health care for rural Canadians by demonstrating underutilization of AMI preventive diagnostic care among rural patients. Rural seniors are most at risk. These results have implications for rural health care as well as seniors' health policy in Canada.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".