Driving Evaluation Practices of Clinicians Working in the United States and Canada
Bibliographic record
Abstract
OBJECTIVE: To determine off-road and on-road driving evaluation practices of clinicians in the United States and Canada who assess individuals with disabilities for fitness to drive. PARTICIPANTS: Participants were 114 clinician attendees at the 2003 annual Association of Driver Educators for the Disabled with driving assessment experience ranging from 1 month to 25 years. MEASURES: Information was elicited regarding the clinician, clientele, referral practices, and off-road and on-road driving evaluation practices and retraining practices using a self-administered questionnaire. RESULTS: Participants were largely occupational therapists (68%) who worked in 42 different states and provinces. The most prevalent clientele were persons with traumatic brain injury (97%) and stroke (96%). Testing times greater than 60 min were common for both the off-road (61%) and on-road (49%) evaluations. Commonly performed off-road assessments included the Brake Reaction Timer; Trail Making Test, Parts A and B; and the Motor Free Visual Perception Test, used by 73%, 72%, and 66%, respectively; comprehensive computer-based driving evaluation was rare. Sixty-one percent indicated that all clients underwent on-road evaluation regardless of the off-road results. Finally, 78% used a standard driving route, whereas 24% used a scoring system to evaluate on-road driving. CONCLUSION: Driving assessment in Canada and the United States is multidimensional and time-intensive. Although the domains being assessed are similar across clincians, specific off-road and on-road assessment practices vary greatly. The majority use nonstandardized on-road assessments.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 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".