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Record W2796271325 · doi:10.1111/josh.12622

Later Start, Longer Sleep: Implications of Middle School Start Times

2018· article· en· W2796271325 on OpenAlexaff
Deborah Temkin, Daniel Princiotta, Renee Ryberg, Daniel Lewin

Bibliographic record

VenueJournal of School Health · 2018
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsKensington Health
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentRobert Wood Johnson Foundation
KeywordsBedtimeSleep (system call)PsychologyConfoundingMedicineDemographyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Although adolescents generally get less than the recommended 9 hours of sleep per night, research and effort to delay school start times have generally focused on high schools. This study assesses the relation between school start times and sleep in middle school students while accounting for potentially confounding demographic variables. METHODS: Seventh and eighth grade students attending 8 late starting schools (∼8:00 am, n = 630) and 3 early starting schools (∼7:23 am, n = 343) from a diverse suburban school district completed online surveys about their sleep behaviors. Doubly robust inverse probability of treatment weighted regression estimates of the effects of later school start time on student bedtimes, sleep duration, and daytime sleepiness were generated. RESULTS: Attending a school starting 37 minutes later was associated with an average of 17 additional minutes of sleep per weeknight, despite an average bedtime 15 minutes later. Students attending late starting schools were less sleepy than their counterparts in early starting schools, and more likely to be wide awake. CONCLUSIONS: Later school start times were significantly associated with improved sleep outcomes for early adolescents, providing support for the movement to delay school start times for middle schools.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.043
GPT teacher head0.346
Teacher spread0.303 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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