Experiences of adults with stroke attending a peer-led peer-support group
Bibliographic record
Abstract
Abstract Purpose: Peer-support groups for stroke survivors are often organized and facilitated by health authorities and disability related organizations within rehabilitation programs. However, the benefits of peer-led, peer-support groups have not yet been evaluated. The purpose of this study was to explore participants’ experiences in a community-based, peer-led, peer-support group for stroke survivors. Materials and Methods: Semi-structured interviews were conducted and analyzed following constructivist grounded theory with 11 participants who attended a peer-led, peer-support group for people with stroke. The data were also complemented with one quantitative rating question regarding their experience attending the group. Results: Three themes were identified. Meeting unmet needs after stroke captured how the group was created by stroke survivors to address life in the community post-stroke. Buddies helping buddies highlighted that stroke recovery is a shared process at the group, where members help and encourage each other to contribute what they can. Creating authentic friendships revealed how people experienced social connection and developed relationships in the peer-led, peer-support group. Conclusions: Peer-led, peer-support groups may provide opportunities for stroke survivors to connect with like-minded people in their community to have fun while exploring their abilities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".