Exploring informal elderly mobility scooters as alternative transportation for older adults
Bibliographic record
Abstract
In China’s rapidly aging cities, millions of elderly residents rely on informal mobility scooters to address gaps in formal transportation. However, since 2018, local governments have increasingly prohibited their use under national regulatory directives, creating conflicts between safety regulation and mobility access. This qualitative study draws on semi-structured interviews with 15 elderly scooter users across three Shanghai districts to examine how older adults navigate these restrictions and why they persist in using prohibited vehicles. Thematic analysis identified three interconnected themes: informal mobility adoption driven by service gaps, affordability limits, and digital exclusion; adaptive strategies that allowed older adults to sustain essential travel while reducing enforcement risks; and a conditional approach to compliance, where independence was prioritized but willingness to follow rules was expressed if viable alternatives were provided. These findings highlight that prohibitions without alternatives limit older adults’ ability to meet essential needs such as medical care, caregiving, and community participation. Continued use of scooters reflects practical responses to gaps in transport provision rather than intentional disregard for regulation. Enforcement-led approaches overlook this reality. More flexible strategies, including subsidies for safer vehicles, community-based licensing programs, and designated operational areas, could balance safety with the need for independent mobility. By showing how older adults adapt when formal systems do not meet their needs, the study highlights the importance of regulatory frameworks that respond to these practical challenges instead of penalizing them, and points to the value of incorporating lived experience into the design of age-friendly transport policies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".