Utility of an Occupational Therapy Driving Intervention for a Combat Veteran
Bibliographic record
Abstract
Many combat veterans are injured in motor vehicle crashes shortly after returning to civilian life, yet little evidence exists on effective driving interventions. In this single-subject design study, we compared clinical test results and driving errors in a returning combat veteran before and after an occupational therapy driving intervention. A certified driving rehabilitation specialist administered baseline clinical and simulated driving assessments; conducted three intervention sessions that discussed driving errors, retrained visual search skills, and invited commentary on driving; and administered a postintervention evaluation in conditions resembling those at baseline. Clinical test results were similar pre- and postintervention. Baseline versus postintervention driving errors were as follows: lane maintenance, 23 versus 7; vehicle positioning, 5 versus 1; signaling, 2 versus 0; speed regulation, 1 versus 1; visual scanning, 1 versus 0; and gap acceptance, 1 versus 0. Although the intervention appeared efficacious for this participant, threats to validity must be recognized and controlled for in a follow-up study.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".