Priorities for quality of life after traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: After traumatic brain injury (TBI), individuals can experience changes to quality of life (QOL). Despite understanding the factors that impact QOL after TBI, there is limited patient-oriented research to understand the subjective priorities for QOL after TBI. This study aims to understand the priorities for QOL after TBI using a group consensus building method. METHODS: The Technique for Research of Information by Animation of a Group of Experts (TRIAGE) method was used to determine priorities for QOL after TBI. In phase one, expert participants were consulted to understand the context of QOL after TBI. In phase two, participants with TBI completed a questionnaire to broadly determine the factors that contributed to their QOL. In phase three, a portion of participants from phase two engaged in focus groups to identify the most relevant priorities. Data was analyzed thematically. In phase four, expert participants were consulted to finalize the priorities. RESULTS: Phase one included three expert participants who outlined the complexity and importance of QOL after TBI. Phase two included 34 participants with TBI who described broad priorities for QOL including social support, employment, and accessible environments. Phase three included 13 participants with TBI who identified seven priorities for QOL: ensuring basic needs are met, participating in everyday life, trusting a circle of care, being seen and accepted, finding meaning in relationships, giving back and advocating, and finding purpose and value. In phase four, four expert participants confirmed the QOL priorities. INTERPRETATIONS: Findings emphasize the critical need to address priorities for QOL after TBI to ensure improved health outcomes.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".