A Systematic Review of Effective Interventions and Strategies to Support the Transition of Older Adults From Driving to Driving Retirement/Cessation
Bibliographic record
Abstract
Abstract Background and Objectives In most western countries, older adults depend on private cars for transportation and do not proactively plan for driving cessation. The objective of this review was to examine current research studies outlining effective interventions and strategies to assist older adults during their transition from driver to driving retirement or cessation. Research Design and Methods A search was completed across 9 databases using key words and MeSH terms for drivers, cessation of driving, and older adult drivers. Eligibility screening of 9,807 titles and abstracts, followed by a detailed screening of 206 papers, was completed using the Covidence platform. Twelve papers were selected for full-text screen and data extraction, comprising 3 papers with evidence-based intervention programs and 9 papers with evidence-informed strategies. Results Three papers met the research criteria of a controlled study for programs that support and facilitate driving cessation for older adults. Nine additional studies were exploratory or descriptive, which outlined strategies that could support older drivers, their families, and/or healthcare professionals during this transition. Driving retirement programs/toolkits are also presented. Discussion and Implications The driver retirement programs had promising results, but there were methodological weaknesses within the studies. Strategies extracted contributed to 6 themes: Reluctance and avoidance of the topic, multiple stakeholder involvement is important, taking proactive approach is critical, refocus the process away from assessment to proactive planning, collaborative approach to enable “ownership” of the decision is needed, and engage in planning alternative transportation should be the end result. Meeting the transportation needs of older adults will be essential to support aging in place, out-of-home mobility, and participation, particularly in developed countries where there is such a high dependency on private motor vehicles.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".