Peer support for stroke survivors: a case study
Bibliographic record
Abstract
BACKGROUND: Innovative and sustainable programs are required to support the well-being of stroke survivors. Peer support is a potentially low cost way to enhance well-being of recent stroke survivors and the well-being and community reintegration of their peer supporters. This article describes the perceptions of stroke survivors, care partners, peer supporters, and professionals of an individual peer support program. METHODS: An instrumental case study design was used to examine a volunteer peer support program that provides acute care visits and telephone follow-up post-discharge. In particular, a) type of support provided, b) benefits for the stroke survivor and care partner, c) potential harms to the stroke survivor, d) impact of providing support on the peer supporter, and e) required processes were considered. Semi-structured interviews were carried out with 16 new stroke survivors and 8 care partners immediately following hospital discharge and then 6 months later, and with 7 peer supporters, 3 program co-ordinators and 4 health professionals to gather feedback from multiple stakeholders. RESULTS: Emotional, affirmational and informational support were perceived as being offered by the peer supporters. Peer visits were perceived as providing encouragement, motivation, validation, and decreased feelings of being alone. However, the visits were not perceived as beneficial to all stroke survivors. The impact on the peer supporters included increased social connections, personal growth, enjoyment, and feelings of making a difference in the lives of others. Involvement of the healthcare team, peer supporter training and a skilled coordinator were crucial to the success this program. CONCLUSIONS: Peer support can potentially enhance service to stroke survivors and promote community reintegration for peer volunteers. Further research is needed to determine the preferred format and timing of peer support, and the characteristics of stroke survivors most likely to benefit.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".