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Record W6998582307

The association between sleep quality and anxiety among postsecondary students

2022· dissertation· en· W6998582307 on OpenAlexaboutno aff

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2022
Typedissertation
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyAssociation (psychology)Sleep qualitySleep (system call)Cohort studyCohortQuality (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Poor sleep quality has been associated with anxiety in postsecondary students. However, high-quality epidemiological evidence about this association is lacking. 
\nPurpose: To determine whether poor sleep quality is associated with anxiety in undergraduate university students. 
\nMethods: Two studies were conducted. First, a systematic review of the literature was conducted on the association between poor sleep quality and anxiety. Second, the association between poor sleep quality and moderate to extremely severe anxiety was measured in a cross-sectional study in postsecondary students enrolled in two faculties at Ontario Tech University (formerly known as he University of Ontario Institute of Technology) and the Canadian Memorial Chiropractic College during the 2017 fall academic term. 
\nResults: After screening and critically appraising all relevant articles, 27 of 28 studies reported a significant association between sleep quality and anxiety. Results from the cross-sectional study show that students who reported poor sleep quality were more likely to report moderate to extremely severe anxiety. 
\nConclusions: Both studies suggest that poor sleep quality is associated with anxiety in postsecondary students. To determine if poor sleep quality is an independent risk factor for anxiety in Canadian postsecondary students cohort studies are needed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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