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Record W3166846612 · doi:10.1123/jcsp.2020-0048

Exploring the Association Between Sport Participation and Symptoms of Anxiety and Depression in a Sample of Canadian High School Students

2021· article· en· W3166846612 on OpenAlexafffundabout
Jessica Murphy, Karen A. Patte, P.F. Sullivan, Scott T. Leatherdale

Bibliographic record

VenueJournal of Clinical Sport Psychology · 2021
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsBrock UniversityUniversity of Waterloo
FundersInstitute of Population and Public HealthInstitute of Nutrition, Metabolism and DiabetesCanadian Institutes of Health ResearchHealth Canada
KeywordsAnxietyDepression (economics)PsychologyAssociation (psychology)Mental healthContext (archaeology)Clinical psychologyAthletesPhysical activityPsychiatryMedicinePhysical therapyPsychotherapist

Abstract

fetched live from OpenAlex

The mental health benefits of physical activity may relate more to the context of the behavior, rather than the behavior of being active itself. The association between varsity sport (VS) participation, depression, and anxiety symptoms was explored using data from 70,449 high school students from the Cannabis use, Obesity, Mental health, Physical activity, Alcohol use, Smoking, and Sedentary behavior study. The model adjusted for potential covariates; interactions by sex and participation in outside of school sport (OSS) were explored. Overall, 70% and 24% of respondents met or exceeded cutoff values for depression and anxiety, respectively. Students participating in VS had lower symptoms of anxiety and depression compared with nonparticipants. Results were consistent regardless of OSS participation; associations were strongest among students who participated in both VS and OSS and males. Participation in VS may prove beneficial for the prevention and/or management of depression or anxiety symptoms, particularly among males. An additive beneficial effect of OSS on depression and anxiety scores may exist.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
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 head0.201
GPT teacher head0.481
Teacher spread0.280 · 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 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

Citations19
Published2021
Admission routes3
Has abstractyes

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