Evaluation of the effectiveness of three different interventions on older driver safety over a 12-month period: study protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: The growing population of older drivers presents challenges for road safety attributed to age-related declines and increased crash fatality rates. However, enabling older people to maintain their health and independence through continued safe driving is important. This study focuses on the urgent need for cost-effective interventions that reduce crash risk while supporting older drivers to remain driving safely for longer. Our study aims to evaluate the effectiveness of three behavioural interventions for older driver safety. These include an online road-rules refresher workshop, tailored feedback on driving performance and two tailored driving lessons. METHODS AND ANALYSIS: A single-blind three-parallel group superiority randomised controlled trial will be conducted with 198 urban licensed drivers aged 65 years and older, allowing for 4% attrition. This sample size provides 80% power to detect a difference with an alpha of 0.05. Participants will be selected based on a standardised on-road test that identifies them as moderately unsafe drivers. Interventions, spanning a 3-month period, aim to improve driving safety. Their effectiveness will be assessed through a standardised on-road assessment of driving safety at 3 months (T1) and 12 months postintervention (T2). Additionally, monthly self-reported driving diaries will provide data on crashes and incidents.This trial has the potential to identify cost-effective approaches for improving safety for older drivers and contribute to evidence-based health policy, clinical practice and guidelines. ETHICS AND DISSEMINATION: Ethical approval was obtained by the University of New South Wales Human Research Ethics Committee (HC190439, 22 August 2019). The results of the study will be disseminated in peer-reviewed journals and research conferences. TRIAL REGISTRATION NUMBER: ACTRN12622001515785.
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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 | 0.083 | 0.075 |
| Meta-epidemiology (narrow) | 0.011 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.076 | 0.019 |
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".