The Concurrent Criterion Validity of the 32-Item Fitness-to-Drive Screening Measure
Bibliographic record
Abstract
Background. The Fitness-to-Drive Screening Measure© (FTDS) is a free online screening tool that identifies at-risk older drivers. This tool screens for at-risk drivers using proxy rater responses (family, friends, and caregivers) to 54 driving-related items. Consumer usage analysis of the FTDS determined that reducing the time commitment to complete the 54-item FTDS might increase usability and uptake of the tool. To address this need, we used classical test theory and exploratory factor analysis to construct a 32-item version of the FTDS. This study aims to establish the concurrent criterion validity of the 32-item FTDS. Method. Two hundred older driver on-road assessments and 200 caregiver FTDS responses were used to generate a receiver operating characteristic (ROC) curve, in which we plotted the rate of true positives against the rate of false positives, calculated the area under the curve (AUC), and used Youden’s index to identify the optimal cut-point for the 32-item FTDS. In this study, the true positive rate was the 32-item FTDS’ ability to predict a fail when the older driver actually failed the on-road assessment, and the false positive rate was the the 32-item FTDS’ ability to predict a pass when the older driver actually passed the on-road assessment. We computed the sensitivity, specificity, positive predictive value, negative predictive value and total number of misclassifications for the optimal cut-point. Results. The ROC curve results indicated an acceptable AUC, with a magnitude of .75, p <.05. At the optimal cut-point of 4.87, the 32-item FTDS had a sensitivity of .74, specificity of .69, positive predictive value of .30, negative predictive value of .93 and 61 (of 200) misclassifications. Conclusion. Although the 32-item FTDS met the criterion (AUC .75, p <.05.) for good concurrent criterion validity in predicting older driver on-road outcomes, it also misclassified 30% of the drivers and as such may be overly sensitive.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".