Barriers to Driving and Community Integration After Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To examine the relations among driving status, perceptions of barriers to the resumption of driving, and community integration outcomes after traumatic brain injury (TBI). DESIGN: Correlational research using logistic and multiple regression analyses, analyses of variance, and covariance. PARTICIPANTS: Fifty-one survivors of TBI, 6 months to 10 years postinjury. MAIN OUTCOME MEASURES: Driving status postinjury, Community Integration Measure, and Craig Hospital Assessment and Reporting Technique. RESULTS: Perceptions of barriers to driving provided unique information in predicting subjective and objective indices of community integration, even after accounting for other potentially pertinent variables (eg, injury severity, social support, negative affectivity, and use of alternative transportation). Moreover, survivors who had not resumed driving showed poorer community integration than did those who had resumed driving. Social barriers such as directives against driving from significant others accounted for the most variance in survivor driving status. Decisions to cease driving were more common among those with no formal driving evaluation than among survivors who had been evaluated. CONCLUSIONS: Significant others have substantial influence on post-TBI driving outcome. The findings highlight the importance of independent driving to community integration, as well as psychoeducation of survivors and their families.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".