Improving outcomes of driving cessation for older people: Early results from a RCT of the UQDrive groups
Bibliographic record
Abstract
Introduction: Driving cessation has been recognised as having substantial negative impact on the community mobility, role participation, health and wellbeing of older people. The UQDRIVE (University of Queensland Driver Retirement Initiative) group program was developed based on interviews with over 250 older drivers, retired drivers, family members and health professionals. It includes community based interventions to improve lifestyle and quality of life of older people facing driving cessation. A randomised controlled trial of UQDRIVE is underway in South East Queensland, Australia. Methods and materials: People aged 60 years and above, living in the community, who had permanently ceased driving or were planning to do so were randomised to either the UQDRIVE intervention or current practice (no intervention). Measures of wellbeing and lifestyle outcomes were undertaken prior to the intervention, immediately after the intervention and three months later. For intervention participants, individual transport and lifestyle goal setting and evaluation using the Canadian Occupational Performance Measure (COPM©) were undertaken pre and post the intervention. Results: Preliminary results after 12 months of data collection will be described. Goals set by group participants included adjustment to driving cessation, driving safety, and role participation. Preliminary analyses of COPM scores indicate significant differences pre and post in perceived performance (mean difference = 3.22 points; t = 7.11; df = 19; p < 0.0001) and satisfaction with performance (mean difference = 3.53 points; t = 6.52; df = 19; p < 0.0001) scales. Comparison between control group and intervention group scores on episodes away from home, depression, anxiety and life satisfaction will also be reported. Conclusion: This paper provides an overview of the UQDRIVE program, the randomised controlled trial currently underway and presents preliminary findings.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".