Spatial Accessibility to Health Care Services: Identifying under-Serviced Neighbourhoods in Canadian Urban Areas
Bibliographic record
Abstract
BACKGROUND: Urban environments can influence many aspects of health and well-being and access to health care is one of them. Access to primary health care (PHC) in urban settings is a pressing research and policy issue in Canada. Most research on access to healthcare is focused on national and provincial levels in Canada; there is a need to advance current understanding to local scales such as neighbourhoods. METHODS: This study examines spatial accessibility to family physicians using the Three-Step Floating Catchment Area (3SFCA) method to identify neighbourhoods with poor geographical access to PHC services and their spatial patterning across 14 Canadian urban settings. An index of spatial access to PHC services, representing an accessibility score (physicians-per-1000 population), was calculated for neighborhoods using a 3km road network distance. Information about primary health care providers (this definition does not include mobile services such as health buses or nurse practitioners or less distributed services such as emergency rooms) used in this research was gathered from publicly available and routinely updated sources (i.e. provincial colleges of physicians and surgeons). An integrated geocoding approach was used to establish PHC locations. RESULTS: The results found that the three methods, Simple Ratio, Neighbourhood Simple Ratio, and 3SFCA that produce City level access scores are positively correlated with each other. Comparative analyses were performed both within and across urban settings to examine disparities in distributions of PHC services. It is found that neighbourhoods with poor accessibility scores in the main urban settings across Canada have further disadvantages in relation to population high health care needs. CONCLUSIONS: The results of this study show substantial variations in geographical accessibility to PHC services both within and among urban areas. This research enhances our understanding of spatial accessibility to health care services at the neighbourhood level. In particular, the results show that the low access neighbourhoods tend to be clustered in the neighbourhoods at the urban periphery and immediately surrounding the downtown area.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".