The impact of transportation equity on healthcare accessibility for children with asthma
Bibliographic record
Abstract
Equitable access to healthcare facilities is essential to quality of life. However, many vulnerable communities encounter barriers because transportation systems are not designed to serve all residents equally. These disparities are particularly significant for childhood asthma, a public health concern where timely care is essential to prevent adverse outcomes. This study addresses the gaps in understanding how various transportation modes, including public transit, private vehicles, and taxis, influence healthcare accessibility for children with asthma. Using data from 18,393 hospital visits in Calgary, Canada (2010–2021), we evaluate spatiotemporal accessibility across three travel modes, considering emergency and non-emergency healthcare visit scenarios with varying travel cost thresholds through a two-step floating catchment area (2SFCA) method. Horizontal equity is quantified using the Gini coefficient, while vertical equity incorporates socioeconomic factors and asthma prevalence. Our findings reveal that personal vehicles provide the highest and most reliable accessibility, especially during emergencies, whereas public transit frequently fails to meet emergency accessibility demands, particularly at night. Taxis tend to be unaffordable for low-income users but offer comparable accessibility for higher-income travelers in non-emergency contexts. The vertical equity analysis identifies areas characterized by high socioeconomic vulnerability, elevated asthma prevalence, and limited access to healthcare, highlighting zones that warrant targeted interventions to enhance equity in healthcare accessibility.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".