Incorporating the Theory of Planned Behaviour into Distracted Driving: Influencing Factors and Intervention Effects
Bibliographic record
Abstract
This study focuses on the psychological characteristics and empirically tests of the factors influencing distracted driving behaviours. This information is used as a reference for an intervention on dangerous driving behaviours. First, a distracted driving scale is constructed based on the theory of planned behaviour (TPB). The questionnaires are distributed in Chongqing, China, and 321 completed questionnaires are obtained. Data are analyzed using mean-variance analysis, one-way ANOVA, T-test, and multivariate test by SPSS 26.0 to determine the significance of distracted behaviours and demographic variables. We use a structural equation model to determine the path coefficients of each latent variable. Finally, we select the drivers with high tendency of distraction from the results of the questionnaires, conduct a four-stage rational emotional behaviour therapy (REBT) experiment, and use a repeated measures ANOVA analysis to test the validity and persistence of the intervention method. Results show that subjective norm is the most influential psychological factor. There are significant differences between the experimental group (2.38, SD = 0.41) and the control group (2.89, SD = 0.40) in the scores of distractions. This indicates that the distracted behaviour intervention achieves adequate validity and consistency. Educational research on distracted driving behaviour can help identify and correct drivers with high distraction tendency.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".