Analysing spatial accessibility to health care: a case study of access by different immigrant groups to primary care physicians in Toronto
Bibliographic record
Abstract
This article analyses the spatial accessibility of a number of immigrant groups to linguistically diverse primary care (family) physicians in the Toronto Census Metropolitan Area (CMA). The two-step floating catchment area (2SFCA) method, a special type of gravity model, is employed to measure spatial accessibility using Network Analyst in ArcGIS 9.3. The context of this study is the predominantly publicly funded Canadian health-care system and a multicultural urban setting where both the population and the physicians are culturally and linguistically diverse. This article focuses on a total of eight ethnicities: six groups of recent immigrants – from Hong Kong, Iran, Mainland China, Pakistan, Russia and Sri Lanka; and two groups of long-standing immigrants – from Italy and Portugal. It examines the spatial (mis)match between the residential distribution of immigrant populations and the distribution of linguistically appropriate family physicians. The quantitative data analysed in this article include the physician data set from the College of Physicians and Surgeons of Ontario and geo-referenced 2006 Canadian Census data. This article highlights areas of poor accessibility and provides a comparison of the different ethnic groups. It demonstrates the use of the geographical information system (GIS) in public health research and yields important policy implications for public health planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".