Validity of the C-RDS Self-Reported Risky Driving Measure
Bibliographic record
Abstract
This study examined the reliability and validity of the Checkpoints Risky Driving Scale (C-RDS) in relation to the Dula Dangerous Driving Index (DDDI) and an objective measure of risky driving. Naturalistic and survey data were collected over an 18-month period from 42 newly-licensed teenage drivers. Kinematic Risky Driving was operationally defined as the rate of elevated gravitational-force events per 100 miles obtained from accelerometers and global positioning systems. Two self-report measures of risky driving, the C-RDS and the DDDI, were assessed at 6-months, 12-months, and 18-months after licensure. Reliability was examined for each measure with correlations and autoregressive models over three time points. Validity was assessed by correlations between the measures and cross-lagged autoregressive models of the longitudinal association of self-reported measures with Kinematic Risky Driving and vice versa. Both the C-RDS and DDDI measures demonstrated substantial stability over time and were highly correlated with each other. The C-RDS measure was significantly associated with Kinematic Risky Driving. The findings provide evidence for the reliability and validity of C-RDS.
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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 | 0.003 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".