A geospatial approach to understanding inequalities in accessibility to primary care among vulnerable populations
Bibliographic record
Abstract
Many Canadians experience unequal access to primary care services, despite living in a country with a universal health care system. Health inequalities affect all Canadians but have a much stronger impact on the health of vulnerable populations. Health inequalities are preventable differences in the health status or distribution of health resources as experienced by vulnerable populations. A geospatial approach was applied to examine how closely the distribution of primary care providers (PCPs) in London, Ontario meet the needs of vulnerable populations, including people with low income status, seniors, lone parents, and linguistic minorities. Using enhanced two step floating catchment area (E2SFCA) method, an index of geographic access scores for all PCPs and PCPs speaking French, Arabic, and Spanish were separately developed at the dissemination area (DA) level. To analyze how PCPs are distributed, comparative analyses were performed in association with specific vulnerable groups. Geographical accessibility to all PCPs, and PCPs who speak specific minority languages vary considerably across the city of London. Access scores for French- and Arabic-speaking PCPs are found comparatively high (mean = 2.85 and 1.01 respectively) as compared to Spanish-speaking PCPs (mean = 0.47). Additionally, many areas with high proportions of vulnerable populations experience low accessibility. Despite its exploratory nature, this study offers insight into intra-urban distributions of geographical accessibility to primary care resources for vulnerable groups. These findings can facilitate health researchers and policymakers in the development of recommendations to increase levels of accessibility of specific population groups in underserved areas.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".