Constructing the 32-item Fitness-to-Drive Screening Measure
Bibliographic record
Abstract
(FTDS) enables proxies to identify at-risk older drivers via 54 driving-related items, but may be too lengthy for widespread uptake. We reduced the number of items in the FTDS and validated the shorter measure, using 200 caregiver responses. Exploratory factor analysis and classical test theory techniques were used to determine the most interpretable factor model and the minimum number of items to be used for predicting fitness to drive. The extent to which the shorter FTDS predicted the results of the 54-item FTDS was evaluated through correlational analysis. A three-factor model best represented the empirical data. Classical test theory techniques lead to the development of the 32-item FTDS. The 32-item FTDS was highly correlated ( r = .99, p = .05) with the FTDS. The 32-item FTDS may provide raters with a faster and more efficient way to identify at-risk older drivers.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".