Brain injury family intervention for adolescents: A solution-focused approach
Bibliographic record
Abstract
BACKGROUND: Strengths-based approaches are increasingly utilized in health care, but little is known about their practical application in rehabilitation with families after pediatric acquired brain injury (ABI). OBJECTIVE: To describe a strengths-based model, Solution-Focused Brief Therapy (SFBT) and its clinical application to family intervention for adolescents with ABI. METHODS: A literature review highlights a growing movement towards resilience and strengths-based approaches to family intervention after pediatric ABI. The authors introduce the assumptions, tenets, and clinical application of SFBT, which is a competency-based and resource-based model that focuses on family strengths and successes. A direct comparison is made between the traditional medical model and the solution-focused paradigm. RESULTS: Key elements of SFBT are described, including specific strategies, techniques, and its clinical application in the Brain Injury Family Intervention for Adolescents (BIFI-A). The BIFI-A, designed for adolescents with ABI and their families, is a 12-session manualized intervention that encompasses education about ABI, skill building, and emotional support. CONCLUSIONS: Given the increased interest for research regarding strengths-based approaches in pediatric rehabilitation, the utilization of SFBT with families of adolescents with ABI warrants further attention and investigation. The BIFI-A, with its underpinnings of SFBT, is a promising new family system intervention that also merits further research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".