Transportation and Aging: Exploring Stakeholders’ Perspectives on Advancing Safe Mobility
Bibliographic record
Abstract
Issues of safe transportation for older adults are multifaceted and must include multiple perspectives if significant progress is to be made in the next decade as the baby-boomers begin to reach age 70. Previous work has explored the main barriers that older adults face in terms of maintaining safe mobility, promising practices for overcoming these barriers, and pressing research needs in the area that still need to be addressed. This paper expands upon this work by addressing five system-wide issues identified by a range of stakeholders that impact the success of policies and programs designed to enhance safe mobility for older adults: collaboration and communication; economics of driving reduction and cessation; the role of traffic safety culture in maintaining safe mobility; the environment; and knowledge and education. Each of these issues is discussed based on the empirical literature. The intent of this article is to improve the safe mobility of older adults by fostering a more focused and stakeholder driven research agenda for current transportation scientists, inform agents of the aging network of service opportunities, and create plausible opportunities for policy making.
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| 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".