Psychometrics of the Fitness-to-Drive Screening Measure
Bibliographic record
Abstract
We employed item response theory (IRT), specifically using Rasch modeling, to determine the measurement precision of the Fitness-to-Drive Screening Measure (FTDS), a tool that can be used by caregivers and occupational therapists to help detect at-risk drivers. We examined unidimensionality through the factor structure (how items contribute to the central construct of fitness to drive), rating scale (use of the categories of the rating scale), item/person-level separation (distinguishing between items with different difficulty levels or persons with different ability levels) and reliability, item hierarchy (easier driving items advancing to more difficult driving items), rater reliability, rater effects (severity vs. leniency of a rater), and criterion validity of the FTDS to an on-road assessment, via three rater groups (n = 200 older drivers; n = 200 caregivers; n = 2 evaluators). The FTDS is unidimensional, the rating scale performed well, has good person (> 3.07) and item (> 5.43) separation, good person (> 0.90) and item reliability (> 0.97), with < 10% misfitting items for two rater groups (caregivers and drivers). The intraclass correlation (ICC) coefficient among the three rater groups was significant (.253, p < .001) and the evaluators were the most severe raters. When comparing the caregivers' FTDS rating with the drivers' on-road assessment, the areas under the curve (index of discriminability; caregivers .726, p < .001) suggested concurrent validity between the FTDS and the on-road assessment. Despite limitations, the FTDS is a reliable and accurate screening measure for caregivers to help identify at-risk older drivers and for occupational therapy practitioners to start conversations about driving.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".