Rethinking the way we measure access to language-concordant health services for minority language populations: a secondary analysis of publicly available physician and population data in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Providing care in a patient's preferred language improves health outcomes and patient satisfaction. In Ontario, access to French-speaking physicians (FSPs) is estimated using FSP-to-Francophone population ratios and compared with total physician-to-total population ratios. This approach fails to consider the fact that FSPs also serve non-Francophone patients and that Francophones must compete with the entire population to access FSPs. As a result, this approach inaccurately suggests that Francophones have better access to language-concordant care than Anglophones/Allophones. We propose a novel approach to address this issue, enabling unbiased comparisons of access to language-concordant care across linguistic groups. DESIGN: This secondary analysis of publicly available data containing linguistic variables for the Ontario population (Statistics Canada, 2021 Census) and for family physicians (FPs) (College of Physicians and Surgeons of Ontario, January 2024) calculated competition-adjusted ratios and probabilities of accessing language-concordant care. SETTING: Ontario, Canada. PARTICIPANTS: Census and publicly available data on FPs (ie, those providing comprehensive family medicine care to the community) and the Ontario population were obtained. RESULTS: Province-wide, the crude ratio of FSPs per 1000 Francophones was 3.46. After adjusting for competition, the ratio of FSP per 1000 population was 0.12, compared with a general physicians-per-1000 population ratio of 1.05. Anglophones/Allophones attached to a FP have a 100% probability of receiving care in English compared with an 11.4% probability for Francophones to receive care from a FSP. Expressed otherwise, Anglophones/Allophones are 8.8 times more likely to receive language-concordant care (ie, care in English) than Francophones. CONCLUSIONS: Although crude physician-to-population ratios overestimate Francophones' access to FSPs, competition-adjusted ratios and probabilities demonstrate that they are much less likely to access language-concordant care than Anglophones/Allophones. This novel approach has equity implications for health human resources planning and can be applied to other linguistic minority groups and healthcare providers.
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| 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".