CHANGES IN DRIVERS’ READINESS FOR MOBILITY TRANSITION, SELF-RESTRICTION, AND HEALTH
Bibliographic record
Abstract
Many older drivers will eventually stop driving. Increased readiness to transition to non-driving status mitigates some of the adverse consequences of driving cessation. The Assessment of Readiness for Mobility Transition (ARMT, Meuser et al., 2011) measures attitudinal and emotional preparedness to transition to non-driving. Using data from the Candrive cohort study, we examined changes over time in older drivers’ readiness to transition to non-driving in relation to changes in driving and health-related variables. A sub-sample of the Candrive cohort was recruited from 4 Canadian sites (N=183, mean age at baseline = 77.60 years, SD=4.52). Participants completed a set of measures annually for three years, including the ARMT, health-related measures (e.g., medications, medical conditions), Activities of Daily Living (ADLs), driving restriction, and driving situational avoidance. There were no statistically significant changes in ARMT scores over the 3-year period, F(2, 298)=.51, p=.603. However, there were statistically significant changes in health status as evidenced by an increase in daily medications, F(2, 286)=5.18, p=.006, and medical conditions, F(2, 286)=13.28, <.001, as well as a decrease in ADLs, F(2, 286)=5.04, p=.007. There was also a statistically significant increase in self-restriction of driving to avoid complex situations, F(3, 274)=10.99, p<.001. No statistically significant associations were observed between changes in ARMT scores and changes in either health or driving-related variables. Unlike health and driving-related variables, ARMT scores were relatively stable over a three-year period. Change in ARMT scores over time appears to be independent from changes in other variables known to be associated with driving.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".