Geospatial analysis of neighbourhood-level primary care attachment in Ottawa, Canada
Bibliographic record
Abstract
Context: Canada has a primary care crisis, with fewer family physicians providing care to meet population needs. Identifying communities with greater disparities in primary care access requires integration of data from a range of sources. Objective: To examine primary care access across neighbourhoods in Ottawa, Canada. Study Design and Analysis: We conducted a cross-sectional study of primary care attachment rates by examining correlations with neighbourhood-level sociodemographic factors of resident age, race, income, and socioeconomic advantage. We used GIS geospatial mapping techniques to visualize attachment rates and distribution of family physicians across ‘natural’ neighbourhoods as defined by the Ottawa Neighbourhood Study. Setting or Dataset: Ottawa, Canada’s capital, with a population of one million residents and approximately 1,700 practicing family physicians. We used a) 2022 primary care attachment rates obtained from the Ottawa Community Health Profiles Partnership, b) neighbourhood-level data from Statistics Canada’s 2021 Census, and c) 2024 registry data from the College of Physicians and Surgeons of Ontario. Population Studied: Residents eligible for publicly-funded provincial healthcare. Outcome Measures: i) The proportion of residents unattached to primary care using a previously validated algorithm; and ii) the ratio of family physicians to residents. Results: 15.6% of Ottawa residents (n=165,362) were unattached to primary care. Rates of unattachment varied significantly across neighbourhoods, ranging from 7.4% to 27.7%. We found a significant gradient in the proportion of unattached residents by neighbourhood disadvantage (11.7% in most advantaged quintile vs 22.1% in least advantaged quintile), although the least advantaged neighbourhoods had the highest ratios of family physician to residents. We observed strong correlations between primary care attachment and other sociodemographic factors, with greater unattachment in neighbourhoods with a greater proportion of residents who were living on a low income (R2 = 0.75), young adults aged 20-34 years (R2 = 0.66), living alone (R2 = 0.55), unemployed (R2 = 0.51), or racialized (R2 = 0.32). Conclusions: Our findings highlight the influence of socioeconomic factors on primary care attachment and emphasize the importance of targeted interventions to address disparities and promote equitable healthcare access across Ottawa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".