Capturing Voluntary, Involuntary, and Habitual Components of Driver Distraction in a Self-Reported Questionnaire
Bibliographic record
Abstract
To maximize the effectiveness of strategies for mitigating driver distraction, it is crucial to understand the factors underlying drivers’ engagement in distractions. This article describes a step toward an improved version of the Susceptibility to Driver Distraction Questionnaire (SDDQ), namely the development of an exploratory questionnaire based on findings from the original SDDQ. In this exploratory questionnaire, the Theory of Planned Behaviour continues to serve as the framework for investigating voluntary distractions, relating intentional actions to attitudes, perceived behavioural control, and perceived social norms regarding distractions. Involuntary distractions are captured by investigating the difficulty associated with ignoring information that is not critical for safe driving. A new component of habitual behaviours is also added to measure distractions that involve minimal conscious control, yet were once intentional and goal-driven. The resulting exploratory questionnaire will be used in an upcoming online survey study to determine the items that most effectively capture voluntary, involuntary, and habitual distraction. An improved SDDQ will be generated based on analyses of this pending study.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".