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Record W4320917882 · doi:10.1017/brimp.2023.3

Experiences of adults with stroke attending a peer-led peer-support group

2023· article· en· W4320917882 on OpenAlexaff
Carmen May, Katlyn Bieber, Debbie Chow, W. Ben Mortenson, Julia Schmidt

Bibliographic record

VenueBrain Impairment · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsInternational Collaboration On Repair DiscoveriesGF Strong Rehabilitation CentreUniversity of British Columbia
Fundersnot available
KeywordsPeer supportPeer groupSupport groupSocial supportStroke (engine)PsychologyRehabilitationPeer reviewMedicineGerontologyPhysical therapySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.285
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2023
Admission routes1
Has abstractyes

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