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Record W3043486557 · doi:10.7759/cureus.9292

Lights Out! The Body Needs Sleep: Electronic Devices and Sleep Deficiency

2020· review· en· W3043486557 on OpenAlexaboutno aff
Nicholas Tsouklidis, Nayibeth Tallaj, Yinabeth Tallaj, Stacey E Heindl

Bibliographic record

VenueCureus · 2020
Typereview
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSleep hygieneMedicineSleep (system call)ObesityBody mass indexGerontologyHygienePsychiatryEnvironmental healthPediatricsSleep qualityInsomniaEndocrinology

Abstract

fetched live from OpenAlex

Sleep hygiene in children and young adults has been a topic of interest in scientific studies geared towards understanding metabolism, mental health, neuroscience, and in reference to the quality of life. There are multiple factors that may contribute to poor sleep hygiene in children, many of these include environmental factors and genetic components. This review article will pay particular focus on environmental factors which as of late, have been increasing contributors to poor sleep hygiene in children. Ultimately, these factors lead to unhealthy habits that transform into unhealthy lifestyles in younger populations worldwide. This article will concentrate on studies conducted in the United States, Canada, Switzerland, Norway, and Belgium. In each of these studies, children who are exposed to increased use of electronic devices such as tablet computers, television, desktop computers, and other mobile devices during the late hours of the night, are evaluated and assessed for changes in their rapid eye movement (REM) sleep cycles, sleep latency, body mass index (BMI) levels, obesity risks, and other neurologic deficits which may be linked to this inappropriate use of technology during peak hours of the night. We will understand the physiology behind how sleep works, the events leading up to sleep, and disruptions that can occur, and their devastating effects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.024
GPT teacher head0.320
Teacher spread0.297 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2020
Admission routes1
Has abstractyes

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