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Record W3197932488 · doi:10.1123/jcsp.2020-0037

Exploring the Relationship Between Sleep Quality, Sleep Hygiene, and Psychological Distress in a Sample of Canadian Varsity Athletes

2021· article· en· W3197932488 on OpenAlexaffabout
Jessica Murphy, Christopher Gladney, Philip Sullivan

Bibliographic record

VenueJournal of Clinical Sport Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsBrock University
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexSleep hygieneAthletesPsychologySleep (system call)Sleep qualityClinical psychologyMental healthDistressPsychological distressPhysical therapyPsychiatryInsomniaMedicine

Abstract

fetched live from OpenAlex

Student athletes balance academic, social, and athletic demands, often leading to increased levels of stress and poor sleep. This study explores the relationship between sleep quality, sleep hygiene, and psychological distress in a sample of student athletes. Ninety-four student athletes completed the six-item Kessler Psychological Distress Scale (K6), Sleep Hygiene Practice Scale, and four components from the Pittsburgh Sleep Quality Index. Age, gender, and sport were also collected. The Pittsburgh Sleep Quality Index revealed that 44.7% of student athletes received ≥6.5 hr of sleep each night; 31% of athletes showed signs of severe mental illness according to the K6. Stepwise regression predicted K6 scores with the Pittsburgh Sleep Quality Index and the Sleep Hygiene Practice Scale scores as independent variables. A significant model accounting for 26% of the variation in K6 scores emerged; sleep schedule and sleep disturbances were significant predictors. Athletic staff should highlight the importance of sleep for mental health; suggestions on how to help athletes are provided.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.048
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.402
GPT teacher head0.475
Teacher spread0.073 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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