Primary care access in Ontario, Canada: Geographical patterns of primary care attachment
Bibliographic record
Abstract
Context: The Ontario government recently implemented Ontario Health Teams (OHTs), which are nongeographic patient-provider networks. Each Ontario resident is attributed by the government to an OHT based on care-seeking patterns. Our research team has produced data on patterns of primary care attachment by OHTs, providing key information about each OHT’s attributed population and serving as an important resource for applied health services researchers, health care administrators and health policy decision makers. A key gap, and one of the most frequently cited needs from policy makers and service providers, is to provide this same data at a more granular geographical level, facilitating an understanding of attachment patterns at the neighbourhood or local community level. Objective: Using Ontario data on primary care attachment, map community level patterns of attachment and identify key differences in attachment across communities. Study Design and Analysis: Cross-sectional study using linked health administrative data in conjunction with measures of attachment to a primary care provider. Attachment data and maps are provided for 526 Ontario forward sortation areas (FSAs), which are geographical units based on the first three digits of a Canadian postal code. Additional health utilization data and rank order of regions most poorly served by primary care are provided. Datasets: Linked population-based health administrative datasets in Ontario, Canada. Population Studied: All 14.9 million residents of Ontario, Canada meeting study inclusion criteria (e.g., alive, and contact with health care system within 7 years) as of March 31, 2022. Outcome Measures: Patient attachment to primary care. Residents were classified as attached if they use any community health centre (salary- and interdisciplinary team–based model), were enrolled in a primary care program (team–based, blended fee-for-service, blended capitation, or other model) or visited a primary care physician who did not have low continuity (suggesting a walk-in practice). All others were considered uncertainly attached. Results: There was a major variation across the province in those patients who did not have regular access to primary care. In Southern Ontario, large urban centers had higher proportions of uncertainly attached residents. Conclusions: These results help to inform policy makers on where to focus funding and resources to target communities that are under-served in primary 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.016 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".