Test–Retest Reliability of the Susceptibility to Driver Distraction Questionnaire
Bibliographic record
Abstract
The Susceptibility to Driver Distraction Questionnaire (SDDQ) investigates voluntary and involuntary factors associated with driver distraction. The questionnaire consists of 39 items in six subscales: (a) self-reported distraction engagement, (b) attitudes toward distractions, (c) perceived control of driving while engaged in distractions, (d) injunctive social norms associated with distraction engagement, (e) descriptive social norms associated with distraction engagement, and (f) susceptibility to involuntary distractions. A sample of 43 adults, ages 25 to 39 years, was used to assess the test–retest reliability of the SDDQ. The mean time between test and retest conditions was approximately 20 days. For sub-scale averages, intraclass correlation (ICC) statistics were used to assess test–retest reliability; weighted kappa statistics were used to assess individual items. The ICC results suggest good to excellent test–retest reliability for subscales of self-reported distraction engagement, attitudes toward distractions, and descriptive social norms. Perceived control of driving while engaged in distractions had fair test–retest reliability, and the injunctive norms and susceptibility to involuntary distraction subscales had poor test–retest reliabilities. The latter two subscales may have to be redesigned; this paper provides relevant suggestions for revision in the discussion of the results. As an additional preliminary analysis, data from a sample of 10 additional participants were used to investigate the consistency of responses across longer periods of time. The mean time between test–retest conditions in this sample was approximately 8 months. In general, the findings were similar to those in the main sample. Overall, the SDDQ appears to have good test–retest reliability. A larger sample is recommended for further validation of these results, in particular across long test–retest periods.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".