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Record W2496067131 · doi:10.1080/17518423.2016.1211190

Characterizing socially supportive environments relating to physical activity participation for young people with physical disabilities

2016· article· en· W2496067131 on OpenAlexafffund
Tara Joy Knibbe, Elaine Biddiss, Brenda Gladstone, Amy C. McPherson

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2016
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsHospital for Sick ChildrenHolland Bloorview Kids Rehabilitation HospitalPublic Health OntarioToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsThematic analysisPsychologyInclusion (mineral)Physical activitySocial supportPhysical disabilityQualitative researchTeamworkSocial engagementApplied psychologySocial psychologyDevelopmental psychologySociologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: To explore the experiences of young people with physical disabilities relating to social inclusion and physical activity, in order to describe the characteristics of social environments that support participation in physical activity. METHOD: An iterative, qualitative design employed in-depth, semi-structured interviews with young people with physical disabilities aged 12-18 (n = 11). Data were analyzed using interpretive thematic analysis. RESULTS: Young people described several ways that their social environments help motivate and support them in their physical activity participation. These include providing: fair and equitable participation beyond physical accommodations; belonging through teamwork; and socially supported independence. CONCLUSIONS: Supportive social environments characterized by equitable participation, a sense of belonging, and opportunities for interdependence, play a critical role in promoting the health and well-being of young people with physical disabilities. These characteristics are important to consider in the design of both integrated and dedicated physical activity programs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.591
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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 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

Citations15
Published2016
Admission routes2
Has abstractyes

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