Susceptibility to Driver Distraction Questionnaire
Bibliographic record
Abstract
Driver distraction significantly impairs performance and increases the likelihood of vehicle crashes. Understanding the underlying reasons for distraction engagement as well as individuals’ susceptibility to various types of distractions is a necessary step in developing effective solutions for mitigating distraction. This paper describes the development and initial evaluation of a questionnaire, the Susceptibility to Driver Distraction Questionnaire (SDDQ), which investigates distraction involvement by making a distinction between voluntary and involuntary engagement in secondary activities, or distractions, as referred to in this paper. The paper presents the theoretical underpinnings, the questionnaire itself, as well as the results of an online survey that examined the reliability and validity of the newly developed questionnaire. The analyses show moderate to high levels of internal consistency among the questionnaire items; this consistency provides support to the reliability of the SDDQ. The results also suggest that self-reported engagement in driver distraction is correlated with other self-reported, unsafe driving behaviors. As expected, personality is associated with attitudes and beliefs that motivate voluntary engagement in distraction, while susceptibility to involuntary distraction is related to cognitive limitations. These results indicate that the SDDQ can potentially be a useful tool to study driver distraction and the underlying reasons for distraction engagement.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".