A driving cessation program to identify and improve transport and lifestyle issues of older retired and retiring drivers
Bibliographic record
Abstract
BACKGROUND: This study explored the transport and lifestyle issues of older retired and retiring drivers participating in the University of Queensland Driver Retirement Initiative (UQDRIVE), a group program to promote adjustment to driving cessation for retired and retiring older drivers. METHODS: A mixed method research design explored the impact of UQDRIVE on the transport and lifestyle issues of 55 participants who were of mean age 77.9 years and predominantly female (n = 40). The participants included retired (n = 32) and retiring (n = 23) drivers. Transport and lifestyle issues were identified using the Canadian Occupational Performance Measure and rated pre- and post-intervention. RESULTS: Paired t-tests demonstrated a statistically significant improvement in performance (t = 10.5, p < 0.001) and satisfaction (t = 9.9, p < 0.001) scores of individual issues. Qualitative content analysis identified three categories of issues including: protecting my lifestyle; a better understanding of transport options; and being prepared and feeling okay. CONCLUSIONS: Participation in UQDRIVE had a positive and significant effect on the issues of the participants. The results highlight that although all participants stated issues related predominantly to practical concerns, there were trends in the issues identified by the drivers and retired drivers that were consistent with their current phase of the driving cessation process.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".