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Record W4412932008 · doi:10.1016/j.qrmh.2025.100017

Navigating the life stage after stroke: From Life 2.0 to stroke prevention models of care — A qualitative exploration of younger and middle-aged adult stroke patients' experiences and recommendations

2025· article· en· W4412932008 on OpenAlexafffund
Sarah Ibrahim, Danielle D’Amico, Lindsey Zhang, Syeda Shanza Hashmi, Angela Verven, Sharon Ng, Troy Francis, Aleksandra Stanimirovic, Jasper R. Senff, Sanjula Singh, Jonathan Rosand, Leanne K. Casaubon, Keithan Sivakumar, Valeria E. Rac, Aleksandra Pikula

Bibliographic record

VenueQualitative Research in Medicine & Healthcare · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsToronto Western HospitalOntario Brain InstituteUniversity of OttawaBaycrest HospitalUniversity Health NetworkUniversity of TorontoToronto General HospitalMcGill University Health CentrePublic Health Ontario
FundersUniversity of Toronto
KeywordsStroke (engine)Qualitative researchStage (stratigraphy)MedicineGerontologyPsychologyPhysical medicine and rehabilitationSociologyEngineering

Abstract

fetched live from OpenAlex

Background Global stroke incidence has been rising among adults 65 years of age or younger. A dearth of research exists exploring and understanding younger and middle-aged adults’ lifestyle-related knowledge and habits along with associated facilitators and/or barriers with the adoption, maintenance, and support needs for development of new brain health interventions, which this study sought to address. Methods A qualitative study was conducted, followed by virtual, semi-structured focus groups. Data collection and analysis were performed using Goffman's dramaturgical theory to guide the inductive thematic data analysis. Results A total of 12 participants comprised the sample. Four themes emerged: 1) Front stage: Life 2.0 , 2) Back stage: Unseen and invisible challenges , 3) Scripts and audience reaction: Dualism of social influence ; and 4) Setting: Standard of care, but to who’s standard? Conclusion Findings contributed to a deeper understanding of factors influencing the adoption of healthy habits and approaches to reconceptualize and re-design brain health interventions that meet the needs, preferences, and priorities of this population.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.208
GPT teacher head0.542
Teacher spread0.334 · 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 designQualitative
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

Citations1
Published2025
Admission routes2
Has abstractyes

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