A systematic review of intervention approaches for driving cessation in older adults
Bibliographic record
Abstract
OBJECTIVE: The aim of this project was to review the literature on interventions aimed at facilitating driving cessation in older adults, with and without dementia. METHODS: A literature search was performed using the databases MEDLINE, CINAHL, Cochrane Central, Embase, and PsycINFO, from 1994 to September 2014. Two independent raters screened articles for inclusion and extracted study data. We only included articles if they directly addressed the topic of intervention approaches to facilitate the process of driving cessation in older adults or to support the adaptation of older adults who have had to stop driving and included a control group. RESULTS: Of an initial 477 unique records identified, 111 pertained to driving cessation in older adults, and only three articles were controlled trials of intervention approaches related to driving cessation. One article described an intervention for retired drivers with dementia, while another was aimed at caregivers of drivers with dementia, and the third included retired and retiring drivers without dementia. Outcomes such as reduced depressive symptoms, increased trips out of home, and efficacy in dealing with the driving cessation process were positive, but the specific outcome measures and magnitude of effects varied across studies. CONCLUSIONS: Although the results summarized in this review point toward potentially promising effects of interventions for facilitating driving cessation in older adults, these findings must be interpreted with caution given the significant methodological limitations of the studies, including small samples, participant attrition, lack of blinding, and non-validated outcome measures. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".