Practices Used by Occupational Therapists and Others in Driving Assessment Centers for Determining Fitness-to-Drive: A Case-Based Approach
Bibliographic record
Abstract
. Aims: The purpose of this study was to examine practices used in driver assessment centers for determining fitness-to-drive (FTD) an automobile using a case-based approach. Methods: Each assessor (N = 46; 89% of whom were occupational therapists) identified if and how they would assess each of the following cases: (1) a 35-year-old man with paraplegia; (2) a 53-year-old woman post stroke; (3) an 82-year-old man involved in a collision; and (4) a 33- year-old woman with schizophrenia. Results: Over 90% would assess cases 2 and 3, but only 72% and 62% would assess cases 4 and 1, respectively. The average number of off-road tests they would use ranged from 1 to 24 and was highest for case 2 (14 ± 4.6) and lowest for case 1 (10.6 ± 3.4). Over 75% of respondents indicated they would do an on-road test in all four cases. Conclusions: This case-based approach provided further insight into how FTD assessments and ensuing recommendations are tailored for different clientele.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".