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Record W4405187292 · doi:10.1080/10413200.2024.2437185

Special Olympics athletes’ experiences of mental health and help-seeking

2024· article· en· W4405187292 on OpenAlexafffund
Jeemin Kim, Chloe Ellard, Katherine A. Tamminen, Kelly P. Arbour‐Nicitopoulos

Bibliographic record

VenueJournal of Applied Sport Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council
KeywordsAthletesPsychologyMental healthApplied psychologySport psychologyClinical psychologyPsychiatryPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Existing research has identified the prevalence, as well as protective factors related to athlete mental health. This study expanded the research on athlete mental health by exploring Special Olympics athletes’ mental health experiences. Semi-structured interviews were conducted with seven female and five male Special Olympics athletes (13–58 years old). Data were analyzed with an interpretive description approach. The results revealed that Special Olympics provided contexts where athletes felt competent and socially connected, which enhanced their mental health. Athletes considered help-seeking as helpful and sought help mostly from informal sources that they could trust (e.g., caregivers, Special Olympics coaches). Healthcare providers (e.g., counselors) were viewed as experts, but were difficult to access. Some athletes reported fear of others’ negative reactions to help-seeking. As mental health disparities persist against individuals with intellectual disability, continued work is necessary to promote equitable access to healthcare and the mental health of individuals with intellectual disability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.353
Teacher spread0.330 · 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.

Study designNot applicable
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

Citations2
Published2024
Admission routes2
Has abstractyes

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