Distance to primary care and its association with health care use and quality of care in Ontario: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: In Canada, patients who move may choose to stay on their original family physician's roster, creating long distances to seek primary care. We sought to explore how distance to primary care affected health care use and quality of care. METHODS: We conducted a population-based study in Ontario, Canada, including urban and suburban patients enrolled with a family physician as of Mar. 31, 2023. The primary exposure was patients' travel distance to their physician. Outcomes included emergency department visits, primary care visits, continuity of care, and cancer screening rates. RESULTS: We included 9 967 955 patients. Of these, 1 261 112 (12.7%) patients lived farther than 30 km from their family physician. These patients had greater odds of having nonurgent emergency department visits in the past year (odds ratio [OR] 1.43, 95% confidence interval [CI] 1.42 to 1.44); having no visits with any family physician in the previous 2 years (OR 1.28, 95% CI 1.27 to 1.28); and not having had screening for colon cancer (OR 1.17, 95% CI 1.16 to 1.18), breast cancer (OR 1.24, 95% CI 1.23 to 1.25), and cervical cancer (OR 1.17, 95% CI 1.16 to 1.18). INTERPRETATION: Among Ontario patients living in urban or suburban areas and rostered to a family physician within a patient enrolment model, more than 10% of patients resided farther than 30 km from their family physician. Proximity to primary care was associated with higher use of primary care, reduced emergency department use, and increased uptake of recommended cancer screening, underscoring the importance of reforms that enhance access to care close to home.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".