Factors Affecting Leisure Participation After a Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To explore leisure participation by people with traumatic brain injury (TBI) and reasons underlying changes after the trauma. PARTICIPANTS: Twenty-six individuals with mild to severe TBI. MAIN MEASURE: Leisure Profile, a semi-structured questionnaire measuring involvement in leisure activities before and after TBI (frequency of activities, degree of interest, and desire to modify one's leisure activities), attitudes toward leisure, and difficulties that might influence leisure activities (impairments and environmental obstacles). RESULTS: Leisure participation was greatly disrupted after TBI, with 92% of the participants reporting a reduction posttrauma. Less severe injuries, more time since the injury, and the presence of social obstacles in the environment were positively correlated with leisure participation. Motor impairments had a negative impact on leisure participation. CONCLUSION: TBI has a significant negative effect on leisure participation. Leisure activities should be evaluated and included in a therapy program designed to promote reintegration into society and work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".