An Exploratory Study on the Predictive Elements of Passing On-the-Road Tests for Disabled Persons
Bibliographic record
Abstract
OBJECTIVES: Driving evaluations are performed by Occupational Therapists to evaluate drivers with disability. They include both off-road and on-road assessments. Many aspects of driving are examined during the on-road assessment. The main objective of this study is to identify the elements of the Occupational Therapy on-road driving assessment that are most predictive of the overall driving evaluation. METHODS: This retrospective cohort study took place at a provincially approved Driving Assessment Program. Records of 700 participants with various disabilities who completed a driving assessment between 1995 and 2003 were reviewed. Only clients who completed the on-road assessment were included in the study. At our center, 11 driving elements comprised of 34 items were used as independent variables and rated as pass (acceptable or good) or fail (borderline or poor). Analysis was completed with descriptive statistics and use of logistic regression to identify elements that contributed most significantly to the overall driving evaluation. RESULTS: A total of 628 clients completed the on-road assessment with an overall pass rate of 50%. Logistic regression modeling identified poor anticipation of road hazards, observation of environment, improper stopping position, poor visual scanning, poor knowledge of the rules of the road, and increasing age as predictive of failure for all participants. Further analysis grouped subjects according to disability to identify similarities and differences between pass/fail predictors. Both similarities and differences in predictive elements were found between cognitive and physical diagnostic groupings. Most notably, the physical diagnostic grouping showed that cognitive, not physical elements of the on-road test, predicted failure of the overall driving evaluation. CONCLUSIONS: Of the 11 elements considered in the on-road evaluation, specific cognitive ones such as anticipates potential hazards, scanning, observes for pedestrians, and proper stopping position tend to contribute more to the prediction of pass or fail than others. These elements should be considered as components of on-road assessments by other Driving Assessment Programs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".