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Record W4288069698 · doi:10.1016/j.sleepe.2022.100038

Sleep parameters associated with university students’ grade point average and dissatisfaction with academic performance

2022· article· en· W4288069698 on OpenAlexaff
Sophie Desjardins, Marjorie Grandbois

Bibliographic record

VenueSleep Epidemiology · 2022
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexSleep (system call)Logistic regressionPsychologySleep qualityClinical psychologyAssociation (psychology)MedicinePsychiatryInsomniaComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

The present study sought to determine which subjective sleep assessment parameters were most strongly associated with university students’ grade point average (GPA) and level of satisfaction with their academic performance. One hundred and five students completed the Pittsburgh Sleep Quality Index and answered questions about their GPA and level of satisfaction with that average. Logistic regression analyses indicate that the parameters most strongly associated with students’ GPA are, in descending order, sleep efficiency, daytime dysfunction due to sleepiness, and total sleep time. Only one parameter was associated with dissatisfaction with the GPA: subjective sleep quality. This study highlights the importance of considering students’ expectations of their academic performance rather than focusing solely on their grades. It also advocates for promoting high sleep efficiency rather than focusing exclusively or primarily on sleep duration.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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