Driving with Parkinson’s Disease: Exploring Lived Experience
Bibliographic record
Abstract
A growing body of literature has explored the impact of Parkinson's disease (PD) on fitness to drive. As such, evidence now supports the use of specific clinical tests for screening purposes, the predictive validity of risk impressions, and the critical driving errors that predict on-road pass/fail outcomes in this population. However, little is known about the lived experiences of persons with PD as they navigate driving-related concerns such as driving impairments, cessation, potential threats to independence, and community mobility. This qualitative secondary data analysis aimed to explore the driving-related lived experiences of persons with PD. We utilized summative content analysis to identify themes related to driving from transcribed interviews with nineteen community-dwelling individuals with PD who participated in the primary study. Five themes emerged within the analysis: (1) the meaning and significance of driving; (2) driving cessation; (3) modified driving behaviors; (4) factors affecting driving; and (5) accessibility. Participants identified driving as an activity that holds significant importance-both directly (i.e., as a primary activity) and as a means for enabling other activities. This study lays the foundation for the development of client-centred and evidence-informed driving interventions for individuals with PD, as well as the development of driving retirement programs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".