The health and socioeconomic impacts of free transportation initiatives: A scoping review
Bibliographic record
Abstract
Free access to transportation is essential for maintaining excellent health and achieving socioeconomic stability. However, obstacles continue to exist, especially for marginalized and vulnerable groups. This scoping review made use of the approach developed by Arksey and O'Malley (2005) to comprehensively investigate the impacts of free transportation initiatives on health and socioeconomic outcomes. We conducted a comprehensive search in four prominent databases— MEDLINE, PUBMED, PsycINFO and Web of Science—resulting in the identification of 8745 articles. After removing 1236 duplicate articles and title and abstract screening, we evaluated 54 full-text articles to determine if they met the criteria for inclusion. Finally, we selected 28 papers to include in our analysis. Most research on the impacts of free transportation programs were from USA (4), United Kingdom (15), Canada (3), Australia (1), South Korea (1), Estonia (1), Taiwan (1), Finland (1) and Ethiopia (1). The findings showed multiple impacts of free transportation programs ranging from its benefits in enhancing accessibility to healthcare services, improving physical activity, and improvements in overall health outcomes such as obesity, depression, and cardiovascular diseases. The socioeconomic impacts include improved opportunities for employment, better access to educational opportunities, improved social inclusion, increased engagement in community activities, economic advancement, and better access to essential services. The positive impacts of these initiatives often benefit equity seeking groups. This scoping review demonstrates how free transportation initiatives can effectively reduce health inequities and enhance wellbeing. It also provides evidence of the need for continued research and policy approaches to improving the planning and implementation of free transportation projects for improving wellbeing. • Free transportation improves physical activity and reduces stress. • Free transportation schemes are more beneficial for vulnerable groups such as women and ethnic minorities. • Free transportation schemes improve access to key resources and enable social participation. • Free transportation schemes can be scaled up at the national level leading to improved mobility and reduced congestion.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".