Stay SHARP (See, hear, attend, respond, perform) - sustaining and retraining visual-perception, motor and cognitive skills among older drivers : a feasibility project
Bibliographic record
Abstract
The ability to drive an automobile is a central aspect of independent living for many older adults (Ragland, Satariano, & MacLeod, 2004). Much of the research in the field of driving and the elderly has focused on accident situations in which older drivers are over-involved, with little attention placed on developing and evaluating methods to enhance driving abilities. This thesis is comprised of two manuscripts, one examining the effectiveness of retraining programs for older drivers, and the other exploring older adults' perceptions of driving, concerns/difficulties associated with driving, and factors stimulating interest and participation in a driving program. The first manuscript presents a systematic review of the most recent literature on evidence regarding the effectiveness of retraining programs for older drivers. Reviewed articles were grouped according to the intervention studied: physical retraining, visual perception or education. Randomized controlled trials (RCTs) were appraised using the Physiotherapy Evidence Database (PEDro) Scale (PEDro, 2006) and interpreted following Foley's quality assessment (Foley, Teasell, Bhogal, & Speechley, 2003). Each intervention was then rated for effectiveness based on Sackett's levels of evidence (Sackett, Richardson, Rosenberg, & Haynes, 2000). Six RCTs, one pre- post-study design and one descriptive study met the inclusion criteria, one investigating physical retraining, one a visual perception intervention, five using an educational intervention and one examining a combination of all three, in addition to traffic engineering improvements. There is limited evidence that physical retraining (Level 2a) and visual perception retraining (Level 2a) improve driving related skills in older drivers. There is moderate evidence that educational interventions improve driving awareness and driving behavior (Level 1a), but do not reduce crashes (Level 1b) in older drivers. This suggests that while the evidence is limited, it is sufficiently encouraging to merit further research on interventions for healthy older drivers. In the second manuscript, the authors explore activities seniors use driving for, when and where they drive, importance of driving, perceived driving habits, behavioral changes as people age, and factors stimulating interest and participation in a driving program. Three focus groups (n=18), conducted using a structured format, were held with former and current drivers, 75 years and older, living in Montreal, Canada. Discussions were audiotaped, transcribed, and analyzed to identify themes/key points. Participants reported driving for short and long-distance trips, personal and leisure activities, or in situations where walking was not practical or possible. They indicated driving during days and evenings, on city streets and highways. Frequently reported changes and difficulties included reduced evening vision, slowing response times, and road signs not being clear or visible. The principle results indicated that participants were enthusiastic about a driving program and perceived a need for content such as: traffic law refreshers, retraining of driving-related skills, as well as an on-road driving component. An objective, comprehensive clinical assessment and on-road evaluation were also deemed important. Furthermore, participants expressed preference for a program offered sometime between 11am-to-5pm for one-to-two hours, once or twice weekly. This focus group research is the first step in a research agenda aimed at developing effective and practical driving interventions for healthy older drivers based on their desired needs and interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".