Rater Reliability and Rater Effects of the Safe Driving Behavior Measure
Bibliographic record
Abstract
We used Safe Driving Behavior Measure (SDBM) to determine rater reliability and rater effects (erratic responses, severity, leniency) in three rater groups: 80 older drivers (mean age = 73.26, standard deviation = 5.30), 80 family members or caregivers (age range = 20-85 yr), and two driving evaluators. Rater agreement was significant only between the evaluators and the family members or caregivers. Participants rated driving ability without erratic effects. We observed an overall rater effect only between the evaluator and family members or caregivers, with the evaluators being the more severe rater group. Training family members or caregivers to rate driving behaviors more consistently with the evaluator's ratings may enhance the SDBM's usability and provide a role for occupational therapists to interpret proxy reports as an entry point for logical and efficient driving safety interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".