Community partnered peer support after traumatic brain injury: a feasibility case study
Bibliographic record
Abstract
BACKGROUND: Peer support can enhance the rehabilitation experience for people with traumatic brain injury (TBI). Understanding the feasibility of integrating peer support using a co-design approach can ensure effective delivery. This study aimed to evaluate the feasibility of implementing a community-based peer support program for people with moderate to severe TBI using a co-designed approach. METHODS: A case series pre-post feasibility study was conducted in partnership with a community brain injury organisation. Participants were adults who experienced a moderate to severe TBI <12 months earlier. Feasibility was assessed using process (recruitment and retention rates), resource (adherence to intervention), management (implementation fidelity) and scientific indicators (pre-post intervention changes). RESULTS: Three participants were included, and most measures of feasibility were achieved. Process was achieved with a recruitment rate of 60% (3/5) and a retention rate of 100%. Resource feasibility was achieved with all peer support sessions (100%). Management feasibility was met through the completion of a co-created checklist of session management by peer support workers. Scientific feasibility outcomes showed limited change between pre- and post-intervention. CONCLUSION: This study demonstrates the feasibility of a co-designed peer support program for people with TBI. Future research may examine the implementation of peer support to explore program scalability and refine outcome measures to better capture the benefits of a peer support focused intervention.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".