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Record W2771809142 · doi:10.3138/ptc.2016-103

Facilitators of and Barriers to Providing Access to Community-Based Exercise Programmes for Adults with Post-Stroke Aphasia from the Perspective of Programme Representatives

2017· article· en· W2771809142 on OpenAlexaffvenueabout
Meredith Fairbairn, Emily Wicks, Sabrina Ait-Ouali, Olivia Drodge, Dina Brooks, Maria Huijbregts, Diane C. Blonski

Bibliographic record

VenuePhysiotherapy Canada · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAphasiaPerspective (graphical)Stroke (engine)Physical therapyRehabilitationMedicinePhysical medicine and rehabilitationMedical educationPsychologyGerontologyComputer scienceEngineeringPsychiatry

Abstract

fetched live from OpenAlex

Purpose: Individuals with post-stroke aphasia (PSA) engage in inadequate levels of physical activity (PA), hindering physical and psychosocial recovery. The purpose of this study was to identify the extent to which community-based exercise programmes (CBEPs) in the Greater Toronto Area (GTA) are accessible to adults living with PSA and the characteristics of such programmes from the perspective of CBEP representatives. Methods: A cross-sectional, Web-based survey was administered to representatives of CBEPs open to adults post-stroke in the GTA to identify the CBEPs' relevant characteristics and perceived facilitators of and barriers to accessibility. Results: A total of 17 eligible CBEP representatives completed the survey, for a response rate of 32%. The most commonly used exercise instructors were personal trainers (8; 47%). Of the 17 eligible CBEP representatives, 10 (59%) identified a lack of qualified personnel as the main barrier to providing access to adults with PSA. Verbal strategies were among the most commonly used methods of communication with adults with PSA (16; 94%), and written material was the least used (5; 29%). Conclusion: Understanding the common facilitators of and barriers to providing adults with PSA access to CBEPs will assist programme developers to improve the accessibility of CBEPs and facilitate PA in 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 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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.338
Teacher spread0.318 · 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

Citations4
Published2017
Admission routes3
Has abstractyes

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