The Impact of Sustainable Transit Availability on Health Inequality in Canadian Cities, 2006 to 2016
Bibliographic record
Abstract
Focus on sustainable transit has grown in recent years, as Canada invests in active and public transport (Infrastructure Canada 2023), and plans to reduce CO2 pollution (Government of Canada 2022). While the health benefits of commuting by walking, biking or even public transit may seem clear, the impact on health inequality within cities is less so; some researchers claim that strong transit systems equalize access to health care (Abu-Qarn and Lichtman-Sadot 2022), while others argue that uneven implementation of sustainable transit may lead to gentrification in transit-accessible neighbourhoods, leading to worse outcomes for vulnerable residents (Tehrani, Wu, and Roberts 2019). This investigation seeks to determine the impact of sustainable transit availability on health inequality in Canadian cities using cross-sectional regression analysis. We use the gap in the hospitalization rate between the highest and lowest income quintiles as a proxy for health inequality. Walkability is found to be related to a smaller gap, while bikability is associated with a wider gap. This may be explained by bikability and transit being associated with gentrification, mitigating any positive effects they may have had on the gap in hospitalizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".