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Record W2519976089 · doi:10.1111/jar.12287

Understanding Special Olympics Experiences from the Athlete Perspectives Using Photo‐Elicitation: A Qualitative Study

2016· article· en· W2519976089 on OpenAlexafffund
Jonathan A. Weiss, Priscilla Burnham Riosa, Suzanne Robinson, Stephanie Ryan, Ami Tint, Michelle A. Viecili, Jennifer A. MacMullin, R. Shine

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2016
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaHealth CanadaAutism Speaks
KeywordsPhoto elicitationQualitative researchPsychologyApplied psychologySociologyComputer scienceKnowledge managementSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Many individuals with intellectual disabilities experience challenges to participating in organized sport, despite its known benefits. The aim of this qualitative study was to understand the experiences of participating in sport (Special Olympics) from the perspectives of athletes with intellectual disabilities. METHODS: Five participants (13-33 years of age) took part in a photo-elicitation project during a 1-month period. RESULTS: Our thematic analysis of participant photographs and descriptions revealed the following athlete themes: 'Connectedness' and 'Training in Sport'. CONCLUSION: Photo-elicitation was a useful and important tool in assisting athlete participants to communicate their motivations to participate in sport in ways that using traditional verbal interviewing would not.

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)0.008
metaresearch head score (Gemma)0.010
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.480
GPT teacher head0.479
Teacher spread0.001 · 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

Citations32
Published2016
Admission routes2
Has abstractyes

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