Assessment Tools for Evaluating Fitness to Drive: A Critical Appraisal of Evidence
Bibliographic record
Abstract
BACKGROUND: Many office-based assessment tools are used by occupational therapists to predict fitness to drive. PURPOSE: To appraise psychometric properties of such tools, specifically predictive validity for on-road performance. METHODS: A literature search was conducted to identify assessment tools and studies involving on-road outcomes (behind-the-wheel evaluation, crashes, traffic violations). Using a standardized appraisal process, reviewers rated each tool's psychometric properties, including its predictive validity with on-road performance. FINDINGS: Seventeen measures met the inclusion criteria. Evidence suggests many tools do not have cutoff scores linked with on-road outcomes, although some had stronger evidence than others. Implications. When making a determination regarding driver fitness, clinicians should consider the psychometric properties of the tool as well as existing evidence concerning its utility in predicting on-road performance. Caution is warranted in using any one office-based tool to predict driving fitness; rather, a multifactorial-based assessment approach that includes physical, cognitive, and visual-perceptual components, is recommended.
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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 | 0.189 | 0.491 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.033 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".