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Record W3094109097 · doi:10.1111/jar.12817

Belonging through sport participation for young adults with intellectual and developmental disabilities: A scoping review

2020· review· en· W3094109097 on OpenAlexaff
Winnie Mai Zhao, Kirtana Thirumal, Rebecca Renwick, Denise DuBois

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2020
Typereview
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntellectual disabilityConstruct (python library)AthletesPsychologyContext (archaeology)Proxy (statistics)PopulationQualitative researchDevelopmental psychologySociologySocial scienceMedicineDemographyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Research suggests that sport facilitates belonging for diverse athletes. This scoping review characterizes literature on sport participation and belonging for young adults with intellectual and developmental disabilities. MATERIALS AND METHODS: A search of five databases identified 17,497 articles. Selected articles (N = 39) underwent data extraction and analysis guided by a theoretical framework of belonging, outlining four processes through which belonging is experienced by individuals with intellectual and developmental disabilities. RESULTS: Articles originated from developed countries and in the context of Special Olympics (N = 17). Studies commonly used qualitative interviews with proxy respondents. While all studies described at least one belonging process, only 11 studies applied the term "belonging," and no study defined the construct. CONCLUSION: Belonging is not well-conceptualized in sports literature for athletes with intellectual and developmental disabilities. Understanding belonging through sport participation for this population may inform sport-based policies and programming.

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)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0110.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
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 head0.204
GPT teacher head0.460
Teacher spread0.255 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations14
Published2020
Admission routes1
Has abstractyes

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