Effect of psychosocial interventions on risky driving behaviours among offender drivers by using simulated and real driving: study protocol for a non-randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Risky driving behaviour including anger while driving has led to millions of global road traffic crashes, thousands of mortalities and injuries. These losses are much more in middle-income countries, such as Iran. This paper explains methods of data collection in a controlled trial study for evaluating the effect of psychosocial interventions on risky driving by using simulated and real driving. METHODS: This non-randomised controlled trial study will include 180 offender drivers. They will refer to the simulation laboratory by traffic police after their driving licences were suspended. At baseline, all participants will fill five questionnaires including demographic, Driving Anger Scale, Driving Anger Expression Scale, Spielberger's Anger and Manchester Driving Behavioural, and then they will be tested with a driving simulator. Afterwards, they will be allocated to one of three-intervention training arms (mindfulness, meta-cognition and social marketing) or a control arm without any training. Risky driving behaviours will be assessed in three follow-ups after intervention. The primary outcome of interest will be driving offences, recorded by traffic police in two time points: at 6 months and 1 year after the intervention. DISCUSSION: This study examines the effect of three interventions in reducing driving offence. The results can end in a new therapeutic training or a new legislation that should be added to current obligatory training for getting driving licence and can lead to long-term safe driving among Iranian drivers. Future research is recommended to study the cost-effectiveness of these interventions in actual driving in Iran. TRIAL REGISTRATION NUMBER: UMIN000039493.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.072 | 0.012 |
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".