Exploring the Association between Air Pollution and Active School Transportation: Perceptions of Children and Youth in India
Bibliographic record
Abstract
Active school transportation (AST), including walking or cycling, is a common practice across India contributing to physical activity accumulation among children and youth. Despite the proven health benefits of AST, rising air pollution levels may offset these benefits and discourage AST. With climate change and severe heat waves exacerbating poor air quality, this study aimed to examine the association between perceptions of air pollution as a problem and AST among children and youth in India. No previous studies have assessed AST determinants from a child or youth perspective in India; thus, this cross-sectional, observational study surveyed 1042 children and youth from 41 urban and rural schools. Logistic regression models were conducted and stratified by age group, gender, and urban vs. rural location. Children and youth who perceived air pollution to be a problem were less likely to engage in AST (OR = 0.617, 95% CI = 0.412, 0.923, p < 0.001), with AST varying based on age, gender, and location. The perception of air pollution as a problem was associated with a lower likelihood of engaging in AST in the 5- to 12-year age group (OR = 0.366, 95% CI = 0.187, 0.711, p = 0.003) but not in the 13- to 17-year age group. Similarly, males (OR = 0.528, 95% CI = 0.306, 0.908, p = 0.021) and rural residents (OR = 0.569, 95% CI = 0.338, 0.956, p = 0.033) who perceived air pollution as a problem were less likely to engage in AST; however, this association was not found in females or urban residents. These findings highlight the importance of child and youth perceptions of the environment in not only informing public health advisories for air quality and safe outdoor activity, but also for designing targeted interventions considering sociodemographic differences in AST among children and youth in India.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".