A SYSTEMATIC REVIEW OF EFFECTIVE STRATEGIES OR INTERVENTIONS TO SUPPORT DRIVING CESSATION AMONG OLDER ADULTS
Bibliographic record
Abstract
Abstract Driving remains the primary mode of transportation and often the only mode for rural and suburban areas. However, research is clear we all outlive our ability to drive. Nevertheless, as community mobility is essential to health and quality of life, we need to study and invest in understanding how to use and learn alternative means of transportation, especially for the vulnerable aging. A systematic review was designed to examine the literature for evidence-based strategies or interventions to support driving cessation among older adults while maintaining community mobility. The team of 13 international researchers from seven countries used the Covidence software with support of an expert health sciences librarian. Using multiple search terms (e.g., driving retirement; driving cessation; driving transition; interventions; strategies; older adults), 7317 studies were imported for screening with 7059 eliminated; 205 full texts were assessed for eligibility by 2-3 members. With 187 excluded, 19 studies were included and examined by the two primary authors. Results demonstrate low evidence of effectiveness of programmatic interventions, although multiple strategies have been identified. Overwhelmingly, the evidence supports starting very early with discussions about the process of transitioning from driving to being a passenger; particularly true with progressive diagnoses such as dementia. This presentation will highlight the programs currently available, strategies that appear to be successful, and review current toolkits for supporting driving cessation as well as emerging interventions and strategies.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".