Learning to drive with neurological conditions: profile of users of an adapted driver training program and cognitive factors associated with success
Bibliographic record
Abstract
PURPOSE: To describe the sociodemographic and cognitive profile of participants enrolled in an adapted driving program for individuals with neurological conditions, to explore the association between cognitive functioning and driving program outcome, and to describe driving habits after program completion. METHODS: This study combined retrospective chart review and cross-sectional data collection. RESULTS: = 22.2 ± 8.6; 39% women). Driving program was either successful (47%), failed (7%), discontinued (34%), or ongoing (13%). Among 35 participants with complete neuropsychological and driving program outcome data, those who successfully completed the program showed better attention functioning, and better performance relative to global functioning for attention, executive functions, and working memory, compared to those who discontinued/failed the program. Among 21 participants who completed a telephone questionnaire on average 3.7 years after program enrollment, 67% obtained their driver's license and drove regularly. Participants reported high levels of satisfaction with the program. CONCLUSION: These results suggest that approximately half of the persons enrolled in a driver training program designed for learners with neurological conditions, obtain a driver's license; and that attention, and to a lesser extent executive functioning and working memory, are related to driving program success.IMPLICATIONS FOR REHABILITATIONIn individual with neurological conditions, learning how to drive can be challenging.An adapted driver training program, involving collaboration between driving instructors and healthcare professionals, simplification of theoretical learning, and increasing driving practice opportunities, can be effective, both in terms of licensing success and client satisfaction.Conducting a pre-driving program neuropsychological assessment, with identification of cognitive strengths and weaknesses, can provide valuable information for clinicians and driving instructors for optimizing training and predicting outcome.Better performance in attention, and better relative to global cognitive functioning in attention, executive functions, and working memory, are related to higher success rate of an adapted driving program.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".