Development and Validity of Western University’s On-Road Assessment
Bibliographic record
Abstract
Although used across North America, many on-road studies do not explicitly document the content and metrics of on-road courses and accompanying assessments. This article discusses the development of the University of Western Ontario's on-road course, and elucidates the validity of its accompanying on-road assessment. We identified main components for developing an on-road course and used measurement theory to establish face, content, and initial construct validity. Five adult volunteer drivers and 30 drivers with multiple sclerosis participated in the study. The road course had face and content validity, representing 100% of roadway components determined through a content validity matrix and index. The known-groups method showed that debilitated drivers (vs. not debilitated), made more driving errors ( W = 463.50, p = .03), and failed the on-road course, indicating preliminary construct validity of the on-road assessment. This research guides and empirically supports a process for developing a road course and its assessment.
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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 | 0.004 | 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 | 0.000 | 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".