Psychometrics of the Self-Report Safe Driving Behavior Measure for Older Adults
Bibliographic record
Abstract
We investigated the psychometric properties of the 68-item Safe Driving Behavior Measure (SDBM) with 80 older drivers, 80 caregivers, and 2 evaluators from two sites. Using Rasch analysis, we examined unidimensionality and local dependence; rating scale; item- and person-level psychometrics; and item hierarchy of older drivers, caregivers, and driving evaluators who had completed the SDBM. The evidence suggested the SDBM is unidimensional, but pairs of items showed local dependency. Across the three rater groups, the data showed good person (≥3.4) and item (≥3.6) separation as well as good person (≥.93) and item reliability (≥.92). Cronbach's α was ≥.96, and few items were misfitting. Some of the items did not follow the hypothesized order of item difficulty. The SDBM classified the older drivers into six ability levels, but to fully calibrate the instrument it must be refined in terms of its items (e.g., item exclusion) and then tested among participants of lesser ability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".