The Impact of Peer Support for Informal Caregivers of Adults with Moderate to Severe Traumatic Brain Injury: A Scoping Review
Bibliographic record
Abstract
PURPOSE: This scoping review provided a broad overview of the research evidence on the impact of peer support programs for informal caregivers of adults with moderate to severe traumatic brain injury (TBI). MATERIALS AND METHODS: Four online databases were used to identify records from inception to 28 August 2024. Quantitative and qualitative original research publications were included if participants were informal caregivers of an adult with moderate to severe TBI, and receiving peer-to-peer support from other caregivers with lived experience. The Template for Intervention Description and Replication (TIDieR) and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklists were used to chart and report data. RESULTS: = 80). All peer support programs included a TBI-related education component, and were mostly social support groups (80%) delivered online (60%). A range of different measures and outcomes related to wellbeing and quality of life were evaluated. Overall, 80% of studies reported at least one significant quantitative outcome or qualitative impact. DISCUSSION: Findings on the impact of caregiver peer support interventions were mixed, and may be attributable to the diverse nature of intervention features. CONCLUSION: Overall, peer support has a positive impact on outcomes of wellbeing and quality of life for caregivers of adults with moderate to severe TBI. Future peer support programs would benefit from cultural adaptions for translation to international settings.
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".