Self-regulation upon return to driving after traumatic brain injury
Bibliographic record
Abstract
The aim of this study was to explore self-reported driving habits and the factors associated with these within the first three months of return to driving following traumatic brain injury (TBI). Participants included 24 individuals with moderate to severe TBI (post-traumatic amnesia duration M = 33.26, SD = 29.69 days) and 28 healthy age, education, and gender-matched controls who completed an on-road assessment. Driving frequency and avoidance questionnaires were administered to assess premorbid driving, anticipated driving upon resuming, and driving at three months post-assessment. There were no differences between groups for premorbid driving frequency or avoidance. Individuals with TBI anticipated greater reductions in driving frequency, t(29.57) = -3.95, p < .001, and increases in avoidance, U = 171.00, z = -2.69, p < .01. On follow up, significant reductions in frequency, t(48) = -3.03, p < .01, but not avoidance, U = 239.00, z = -1.35, p = .18, were observed. Females were more likely to reduce their driving frequency, rs = -.43, p < .05, while increased anxiety was associated with increased avoidance r = .63, p < .05, and reduced frequency r = -.43, p < .05. It was concluded that individuals with TBI anticipated changes in their driving habits upon return to driving, indicating an expectation for post-injury changes to their driving lifestyle. On follow up, many of these intended changes to driving habits, particularly in relation to driving frequency, were reported by individuals with TBI, suggestive of some strategic self-regulation.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".