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Record W4200298817 · doi:10.1080/02640414.2021.2015908

A longitudinal examination of changes in mental health among elite Canadian athletes

2021· article· en· W4200298817 on OpenAlexafffundabout
Zoë A. Poucher, Katherine A. Tamminen, Catherine M. Sabiston, John Cairney

Bibliographic record

VenueJournal of Sports Sciences · 2021
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaMarathon
KeywordsAnxietyMental healthAthletesPsychologyClinical psychologyDepression (economics)DemographicsElite athletesCoping (psychology)Eating disordersLongitudinal studyPsychiatryMedicineDemographyPhysical therapy

Abstract

fetched live from OpenAlex

This study explored how athletes’ symptoms of mental disorders changed over the course of pandemic year. Predictors of baseline levels and changes in symptoms of mental disorders were also examined. Surveys were completed four times throughout a year by Canadian athletes training for the 2020 Olympics/Paralympics (ntime1 = 186, ntime2 = 142, ntime3 = 123, ntime4 = 108). Surveys included demographics questions, measures of perceived stress, training load, social support, coping, self-esteem, depression, anxiety, and disordered eating. Data were analysed using descriptive statistics and latent growth modelling. The prevalence of mental disorder symptoms was high at baseline and there was no significant change over time. Scores for the three disorders were significantly correlated. Female athletes had higher scores for disordered eating at baseline. Higher levels of perceived stress predicted higher scores on mental disorder measures. Longitudinal tracking of symptoms of mental disorders among elite athletes is important because it allows researchers to explore whether disorder symptomologies change; rates of mental disorder symptoms were high at baseline and stayed high over the course of a year. More research is needed to explore possible gender differences in rates of disorder symptoms, and to understand how those symptoms change over time.

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)0.000
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 score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.339
Teacher spread0.300 · 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

Citations39
Published2021
Admission routes3
Has abstractyes

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