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Losing Sleep Over It: Daily Variation in Sleep Quantity and Quality in Canadian Students' First Semester of University

2009· article· en· W2121148654 on OpenAlexafffundabout
Nancy L. Galambos, Andrea L. Dalton, Jennifer L. Maggs

Bibliographic record

VenueJournal of Research on Adolescence · 2009
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyAffect (linguistics)Sleep (system call)Sleep qualityMultilevel modelDevelopmental psychologyClinical psychologyPsychiatryCognition

Abstract

fetched live from OpenAlex

Daily covariation of sleep quantity and quality with affective, stressful, academic, and social experiences were observed in a sample of Canadian 17–19‐year‐olds in their first year of university. Participants (N=191) completed web‐based checklists for 14 consecutive days during their first semester. Multilevel models predicting sleep quantity and quality from daily experiences indicated that more time on schoolwork, expecting a test, and alcohol use predicted less sleep whereas socializing predicted more sleep. More positive affect and no alcohol use predicted better sleep quality. Models predicting daily experiences from sleep the night before indicated that less sleep preceded increases in negative affect, decreases in schoolwork time, and a higher likelihood of socializing. Better sleep quality preceded increased positive affect, decreased negative affect and stress, and less time on schoolwork. These data are informative for understanding relations between sleep and daily experiences as they occur naturally in first‐year university students.

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)0.002
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 score0.024
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.054
GPT teacher head0.409
Teacher spread0.356 · 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

Citations139
Published2009
Admission routes3
Has abstractyes

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