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Record W3009681815 · doi:10.1177/0008417420905707

Experiences of Occupational Performance in Survivors of Stroke Attending Peer Support Groups

2020· article· en· W3009681815 on OpenAlexvenueno aff
Sachindri Wijekoon, Whitney Wilson, Nicolette Gowan, Ludmilla Ferreira, Chetan P. Phadke, Elizabeth Udler, Tiziana Bontempo

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsPeer supportOccupational therapyStroke (engine)Support groupQualitative researchPsychologyPeer groupSocial supportClinical psychologyCognitionSocial isolationMedicinePsychiatryDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND.: Survivors of stroke often experience environmental isolation and decreased occupational performance after discharge from the hospital. Peer groups benefit psychological, social, and cognitive functioning, though few studies have examined their influence on occupational performance of survivors of stroke. PURPOSE.: This study explores the experiences of occupational performance in survivors of stroke attending an outpatient peer support group. METHOD.: An interpretive qualitative study using semi-structured interviews was conducted with seven survivors of stroke attending an outpatient peer support group. Data was thematically analyzed. FINDINGS.: Four themes related to the experience of peer support on occupational performance emerged: finding hope to return to meaningful occupation, a place for belonging, problem-solving occupational concerns, and finding purpose beyond oneself. IMPLICATIONS.: This research adds to the existing literature that peer support groups help survivors of stroke reengage in meaningful occupations, manage their stroke experience, and move positively through recovery.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.293
GPT teacher head0.483
Teacher spread0.190 · 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.

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

Citations26
Published2020
Admission routes1
Has abstractyes

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