Fatigue After Traumatic Brain Injury and Its Impact on Participation and Quality of Life
Bibliographic record
Abstract
OBJECTIVES: To examine the relationships between post-TBI fatigue (PTBIF) and comorbid conditions, participation in activities, quality of life, and demographic and injury variables. PARTICIPANTS: 223 community-dwelling individuals with mild to severe TBI and 85 noninjured controls. MEASURES: Global Fatigue Index (GFI), Beck Depression Inventory (BDI-II), McGill Pain Questionnaire (MPQ), Pittsburgh Sleep Quality Inventory (PSQI), Participation Objective Participation Subjective (POPS), SF-36, Life-3. METHOD: Data were collected through interviews and administration of self-report measures as part of a study of PTBIF. RESULTS: Fatigue was more severe and prevalent in individuals with TBI, and more severe among women. It was not correlated with other demographic and injury variables. Once overlap in measurement instruments' content was removed, depression, pain, and sleep problems accounted for approximately 23% of the variance in fatigue in those with TBI compared to 58% of the variance in the control group. PTBIF was correlated with health-related quality of life and overall quality of life, but was not generally related to participation in major life activities. CONCLUSIONS: PTBIF has significant impact on well-being and quality of life and cannot be accounted for by comorbid conditions alone, suggesting that it is related to brain injury itself. It appears to be unrelated to demographic and injury variables other than gender. PTBIF does not limit the quantity and frequency of participation. Future research should focus on the relationship between fatigue and the quality of participation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".