The Driving Cognitive Training Centre (DCTC): Testing a Community-based Brain Training Model for Older Drivers
Bibliographic record
Abstract
Driving allows older adults to remain independent. However, age-related changes in visual and cognitive processes can have an adverse impact on driving skills. Research has shown improvements in driving abilities following 10 hours of targeted brain training using Posit Science’s DriveSharp computer program. We sought to determine if i) making this brain training program available in a community setting (the Driving Cognitive Training Centre, DCTC) is an effective means of engaging older drivers in structured cognitive training, and ii) if there are differences in outcomes for those who participate from home instead of at the DCTC.\nOver a 2-week period, 10 participants aged 64-85 completed 10 hours (1 hour/day; 5 days/week) of driving-specific brain training (provided by DynamicBrain, the Canadian partner of Posit Science) at home or at the DCTC. Pre and post-training measures were collected including assessments of self-reported driving, objective and self-reported cognition and training-related motivation. Subjectively reported positive changes following training for both groups included better peripheral vision, increased awareness and focus level. Participants attending the DCTC felt that the commitment they made to a community centre motivated them to complete their daily training; they also appreciated having better equipment, fewer distractions, and staff assistance on-hand. Those who participated from home primarily cited the benefit of convenience.\nThe DCTC was an effective community model, particularly for those individuals who did not have access to appropriate equipment and assistance at home, and may also encourage program adherence. Future directions to engage individuals in community-based programming will be discussed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".