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Record W2559775821 · doi:10.25011/cim.v39i6.27525

Sleep Quality Differs Between Athletes and Non-athletes

2016· article· en· W2559775821 on OpenAlexvenueno aff
Havva Demırel

Bibliographic record

VenueClinical and investigative medicine · 2016
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesMedicinePhysical therapyAnxietySleep qualitySleep (system call)TurkishQuality of life (healthcare)Depression (economics)PsychologyInsomniaPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: Sufficient sleep or sleep of sufficient quality is essential for the health of children, adolescents and adults, as sleep influences almost all dimensions of life. The purpose of this study was to investigate the possible positive effects of sportsmanship on sleep quality and to assess the possible differences in sleep quality between athletes and non-athletes. METHODS: Sedentary or non-athletes subjects (n=103) and athletes (n=93) participated in this study. The Turkish version of Pittsburg Sleep Quality Index was used to assess the points associated with sleep quality of participants before and one month after wet cupping therapy. RESULTS: Athletes had statistically significantly higher Pittsburg Sleep Quality Index parameters compared with non-athletes. CONCLUSIONS: Long-term exercise or physical fitness is advised for better health and a life without stress, anxiety and depression and also for the normal brain function and emotional stability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.160
GPT teacher head0.397
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2016
Admission routes1
Has abstractyes

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