Perceived accessibility and self-rated health: Examining subjective well-being in the suburbs of Scarborough, Canada
Bibliographic record
Abstract
Addressing inadequate accessibility in suburban areas is crucial for reducing inequalities in transportation and improving suburbanites’ well-being. Afterall, insufficient accessibility to health is linked to worse health conditions. Moreover, urban sprawl , subpar transit, and automobile dependency prevail in suburbs, making them loci of inequalities in accessibility and, consequently, of potential worse health outcomes for residents. Due to their predictive capacity, subjective health indicators have been extensively researched. Knowledge on Self-Rated Health’s (SRH) link to accessibility in suburbs, however, is incomplete because the field seldom considers individuals’ perceptions. This article examines the association between accessibility and SRH in suburban areas. Using ordinal logistic regressions and data from a survey in Scarborough, Canada, we investigate if accessibility measures estimated from land-use and transportation network data (estimated measures), perceived accessibility, and perceptions of the built environment are associated with SRH. We explore these connections with different domains of SRH (mental, physical and overall). We find that living in areas with higher estimated accessibility measures is positively correlated with better SRH, whereas unsatisfactory perceived accessibility is negatively associated. Additionally, suburbanites who prioritize access to healthcare nearby have lower odds of having better health, meaning that residents who would like to see healthcare access improve are more likely to have worst SRH. Estimated measures are positively associated with self-rated mental health , while reporting difficulty in paying for transport is negatively associated with physical health. These findings stress how multiple components of accessibility – from estimated to perceived measures– can be associated with people’s well-being. Results illuminate the relevance of considering perceptions, often overlooked, in accessibility and health analysis. Finally, the results put into question if suburbanites’ heterogenous needs are recognized in urban design in a context of recent suburbanization of poverty.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".