Spatial Access by Public Transport and Likelihood of Healthcare Consultations at Hospitals
Bibliographic record
Abstract
As healthcare is a right in Canada, analyzing the distribution of spatial access to medical consultations, which are crucial for the prevention, diagnosis, and early treatment of illnesses, is fundamental to understanding health equity. Spatial accessibility can influence whether individuals can reasonably reach the services they seek. However, as an indicator of potential access, it does not guarantee realized access because of predisposing and need factors. This study examines the relationship between spatial accessibility to hospitals and the likelihood of consulting with a healthcare professional at a hospital in eight Canadian metropolitan regions while controlling for individual characteristics through multilevel regression modeling. Spatial accessibility was computed using the two-step floating catchment area (2SFCA) method. Self-reported consultations and socio-demographic characteristics were obtained from the Canadian Community Health Survey. We found that the likelihood of consultations differed between genders (female OR: 1.133, CI: 1.023–1.255; compared with male) and followed a positive household income gradient (high-income OR: 1.236, CI: 1.094–1.397; middle-income OR: 1.039, CI: 0.922–1.172; compared with low-income), but is not influenced by age. Living in areas with higher spatial accessibility was positively linked to consultations (OR: 1.014, CI: 1.000–1.028), even after controlling for perceived health (OR: 0.540, CI: 0.471–0.621), chronic conditions (OR: 1.738, CI: 1.587–1.904), and having a regular doctor (OR: 1.313, CI: 1.187–1.452). Policies that may improve spatial accessibility to healthcare services through increasing supply, managing demand, and enhancing level of public transport service should be considered to improve individuals’ ability to consult healthcare professionals, potentially leading to better health outcomes.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".