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The Impact of a Hand Training Programme in Chronic Stroke Survivors: A Qualitative Analysis of Participant Perceived Benefits

2019· article· en· W3005556590 on OpenAlexaffabout
Brontë A. Vollebregt, Reinikka Kirsti., Daniel Vasiliu, Andrea Pepe, Shreya S. Prasanna, Jain Anshul, Jane M. Lawrence‐Dewar, Vineet Johnson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsThunder Bay Regional Research InstituteMcGill UniversitySt. Joseph's Care GroupNorthern Ontario Academic Medicine AssociationLakehead University
Fundersnot available
KeywordsRehabilitationStroke (engine)Qualitative researchQualitative analysisChronic strokePsychologyInterpersonal relationshipPhysical therapyGerontologyPhysical medicine and rehabilitationMedicineSocial psychology

Abstract

fetched live from OpenAlex

Stroke is a leading cause of death and disability in Canada. Community-based training programs are a frequently used means of rehabilitation in stroke. Eight participants were recruited (three female) aged between 55 and 82 (M=69.38, SD=9.75), with a length of time post stroke between 5 and 120 months (M=27.67). All participants completed a 6-week hand training program using a novel haptic indirect-feedback hand function device. Individual interviews with the participants were conducted following the completion of the program. A qualitative analysis of individual interviews determined that there are several components of participants' perceived benefits. These components include a sense of community, companionship, functional improvements, and motivation. This is reflective of past research in the area of community training programs, and the results from this study support this approach.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.402
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes2
Has abstractyes

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