Distance, access and equity: a cross-sectional geospatial analysis of disparities in access to primary care for French-only speakers in Ottawa, Ontario
Bibliographic record
Abstract
BACKGROUND: Although language concordance between patients and primary care physicians results in better quality of care and health outcomes, little research has explored inequities in travel burden to access primary care people of linguistic minority groups in Canada. We sought to investigate the travel burden of language-concordant primary care among people who speak French but not English (French-only speakers) and the general public in Ottawa, Ontario, and any inequities in access across language groups and neighbourhood ruralities. METHODS: Using a novel computational method, we estimated travel burden to language-concordant primary care for the general population and French-only speakers in Ottawa. We used language and population data from Statistics Canada's 2016 Census, neighbourhood demographics from the Ottawa Neighbourhood Study, and collected the main practice location and language of primary care physicians from the College of Physicians and Surgeons of Ontario. We measured travel burden using Valhalla, an open-source road-network analysis platform. RESULTS: < 0.001, interquartile range 0.26-1.17 min), but inequities in travel burden between groups were larger among people living in rural neighbourhoods. INTERPRETATION: French-only speakers in Ottawa face modest - but statistically significant - overall inequities in travel burden when accessing primary care, compared with the general population, and higher inequities in specific neighbourhoods. Our results are of interest to policy-makers and health system planners, and our methods can be replicated and used as comparative benchmarks to quantify access disparities for other services and regions across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".