Biopsychosocial factors of quality of life in individuals with moderate to severe traumatic brain injury: a scoping review
Bibliographic record
Abstract
PURPOSE: Individuals with moderate to severe traumatic brain injury (TBI) experience changes in their quality-of-life (QOL) post-injury. Given the vast literature that exists about QOL after TBI, a scoping review was performed to identify the different biopsychosocial factors that affect a person's QOL after a moderate to severe TBI. METHODS: A scoping review was conducted using the following electronic databases: MEDLINE, CINAHL, Embase, and PsycINFO. Terms relating to TBI and QOL were used. RESULTS: There were 7576 articles obtained from the databases, resulting in 535 full-text articles. Ultimately, 52 articles were extracted, which consisted of biopsychosocial QOL factors after TBI. The biopsychosocial factors of QOL after TBI included 19 biological factors (i.e., sex, TBI severity, cognition), 16 psychological factors (i.e., depression, self-efficacy, coping styles), and 19 social factors (i.e., employment, social participation, social support). Factors such as fatigue, self-awareness, transition, and discharge from hospitals are known issues in TBI literature but were minimally reported in studies in this review, identifying them as potential gaps in research. CONCLUSION: Identifying biopsychosocial factors relating to QOL after TBI can enable health services to develop targeted rehabilitation programs for individuals with TBI.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".