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Record W4285206934 · doi:10.1123/jcsp.2021-0101

Sport Participation, Extracurricular Activity Involvement, and Psychological Distress: A Latent Class Analysis of Canadian High School Student-Athletes

2022· article· en· W4285206934 on OpenAlexaffabout
Camille Sabourin, Stéphanie Turgeon, Laura Martin, Scott Rathwell, Mark W. Bruner, John Cairney, Martin Camiré

Bibliographic record

VenueJournal of Clinical Sport Psychology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsNipissing UniversityUniversity of LethbridgeUniversité du Québec en OutaouaisUniversity of Ottawa
Fundersnot available
KeywordsLatent class modelPsychologyPsychological distressDistressAthletesClinical psychologyDevelopmental psychologyMental healthPsychiatryMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Although psychological distress has been shown to increase during adolescence, participation in organized activities may have protective effects. The present study aimed to identify whether there is a relationship between high school student-athletes’ breadth of participation in organized activities and psychological distress, using a latent class analysis. Canadian adolescent-athletes ( n = 930) in Grades 11 and 12 completed an online survey that measured: (a) high school sport participation, (b) community sport participation, (c) nonsport extracurricular activities participation, and (d) psychological distress. The latent class analysis indicated that a two-class model (i.e., Class 1 = narrower breadth, low distress; Class 2 = wider breadth, moderate distress) was most appropriate. Results indicated that despite the divergent probability of organized activity participation, participants in both classes had a low to moderate probability of presenting elevated levels of psychological distress. However, levels of psychological distress were still higher than other Canadian adolescent populations, suggesting that overscheduling could be of concern. Gender and time (i.e., prior/during COVID-19 pandemic) were significant covariates in the model.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.133
GPT teacher head0.470
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.

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

Citations3
Published2022
Admission routes2
Has abstractyes

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