Lights Out! The Body Needs Sleep: Electronic Devices and Sleep Deficiency
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".