Characteristics of peer-based interventions for individuals with neurological conditions: a scoping review
Bibliographic record
Abstract
Peer-based interventions are increasingly popular and cost-effective therapeutic opportunities to support others experiencing similar life circumstances. However, little is known about the similarities and differences among peer-based interventions and their outcomes for people with neurological conditions. This scoping review aims to describe and compare the characteristics of existing peer-based interventions for adults with common neurological conditions. We searched MEDLINE, CINAHL, PsychInfo, and Embase for research on peer-based interventions for individuals with brain injury, Parkinson’s, multiple sclerosis, spinal cord injury, and stroke up to June 2019. The search was updated in March 2021. Fifty-three of 2472 articles found were included. Characteristics of peer-based intervention for this population vary significantly. They include individual and group-based formats delivered in-person, by telephone, or online. Content varied from structured education to tailored approaches. Participant outcomes included improved health, confidence, and self-management skills; however, these varied based on the intervention model. Various peer-based interventions exist, each with its own definition of what it means to be a peer. Research using rigorous methodology is needed to determine the most effective interventions. Clear definitions of each program component are needed to better understand the outcomes and mechanism of action within each intervention.IMPLICATIONS FOR REHABILITATIONRehabilitation services can draw on various peer support interventions to add experiential knowledge and support based on shared experience to enhance outcomes.Fulfilling the role of peer mentor may be beneficial and could be encouraged as part of the rehabilitation process for people with SCI, TBI, Stroke, PD, or MS.In planning peer-based interventions for TBI, Stroke, SCI, PD, and MS populations, it is important to clearly define intervention components and evaluate outcomes to measure the impact of the intervention. Rehabilitation services can draw on various peer support interventions to add experiential knowledge and support based on shared experience to enhance outcomes. Fulfilling the role of peer mentor may be beneficial and could be encouraged as part of the rehabilitation process for people with SCI, TBI, Stroke, PD, or MS. In planning peer-based interventions for TBI, Stroke, SCI, PD, and MS populations, it is important to clearly define intervention components and evaluate outcomes to measure the impact of the 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 | 0.047 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.029 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".