Accessibility to Healthcare via Public Transport across Canada
Bibliographic record
Abstract
The ability to access healthcare services has long been considered a ‘right’ by Canadian citizens, and is protected as such under the Canada Health Act. However, socio-spatial factors can limit access to healthcare services, especially for vulnerable populations. This paper aims to quantify the spatial accessibility to healthcare services by public transport across eight major Canadian metropolitan areas, and compare accessibility to healthcare across vulnerable population groups. Spatial accessibility to general medical and surgical hospitals was measured through a two-step floating catchment area method, taking into account both service-to-population ratios and travel time to these health services. Within cities, accessibility is equitably distributed: residents of vulnerable census tracts generally have greater access to health services by public transport, with the exception of Vancouver. To quantify vertical equity, an indicator was subsequently developed using the Spearman’s rank correlation coefficient between accessibility and vulnerability. Results show that larger metropolitan areas (Calgary, Toronto-Hamilton, and Vancouver) tend to underperform in terms of vertical equity and average accessibility. This research highlights the challenges associated with the suburbanization of poverty in large Canadian metropolitan regions and the need to provide efficient public transport services to reach hospitals located in the periphery. This study is of relevance to researchers, planners and policy-makers wishing to improve accessibility to healthcare, especially for vulnerable populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".