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Record W2172029925 · doi:10.1177/1744629511422137

The feasibility of using a peer-guided model to enhance participation in community-based physical activity for youth with intellectual disability

2011· article· en· W2172029925 on OpenAlexaff
Viviene A. Temple, Heidi I. Stanish

Bibliographic record

VenueJournal of Intellectual Disabilities · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIntellectual disabilityDyadPsychologyIntervention (counseling)Clinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

We examined the feasibility of a using a peer-guided model to foster participation of youth with intellectual disability in community-based exercise. The intervention was designed to address personal barriers to exercise commonly faced by individuals with intellectual disability. Twenty adolescents with mild-moderate intellectual disability and 14 typically developing peers exercised in groups of two or three, 2 days per week for 15 weeks at YMCAs. Each dyad or triad provided reciprocal support during structured, individualized exercise sessions led by certified fitness trainers. Adherence to the program was high and youth with intellectual disability showed a significant reduction in personal barriers. They also felt they had learned new skills and made new friends. Typically developing youth were also positive about their experience as a volunteer. Our findings suggest that a peer-guided exercise model is useful for enhancing participation in community-based exercise.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.294
GPT teacher head0.468
Teacher spread0.174 · 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 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

Citations39
Published2011
Admission routes1
Has abstractyes

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