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Record W3210524391 · doi:10.1123/apaq.2021-0033

Quality of Participation Experiences in Special Olympics Sports Programs

2021· article· en· W3210524391 on OpenAlexaff
Kelly P. Arbour‐Nicitopoulos, Natasha Bruno, Krystn Orr, Roxy H. O’Rourke, F. Virginia Wright, Rebecca Renwick, Kirsten Bobbie, James Noronha

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsPsychologyAthletesAutonomyBelongingnessQuality (philosophy)Developmental psychologyApplied psychologyClinical psychologySocial psychologyPhysical therapyMedicinePolitical science

Abstract

fetched live from OpenAlex

This cross-sectional study examined experiential elements facilitating quality sport experiences for youth (ages 12-24 years) in Special Olympics, and the associated influences of sport program and sociodemographic characteristics. A total of 451 athletes involved in the 2019 Special Olympics Youth Games completed a survey assessing elements of quality participation (autonomy, belongingness, challenge, engagement, mastery, and meaning). The t tests investigated whether athletes with intellectual and developmental disabilities rated elements differently across Traditional and Unified Sport programs. Regression analyses explored whether sport program and sociodemographic characteristics were predictors of these elements. Youth reported high mean scores across the elements, with no significant differences between athletes with intellectual and developmental disabilities in Traditional or Unified Sport. Athletes with no reported disability rated higher autonomy than those who reported disability (p = .01). Women tended to report greater engagement in sport than men (p = .07). Findings provide theoretical and practical insights into quality sport participation among youth in Special Olympics.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.454

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 head0.058
GPT teacher head0.394
Teacher spread0.336 · 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 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

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAdapted Physical Activity QuarterlySame topicInclusion and Disability in Education and SportFrench-language works237,207