Sleep and learning in school children
Bibliographic record
Abstract
Objectives: To discuss physiological factors that affect learning in school children and the harmful effects of major sleep disturbances on that age. Methodology: Articles were analysed between 2020 and 2015 in LILACS, PUBMED, SciELO and MEDLINE databases with the keywords: Sleep and Child; Sleep and Learning; GH and Learning; Memory and Learning; Blood Flow and Learning; Oxygenation and Learning; Immunity and Cognition. Additionally, we also referred to the books "Sleep and Sleep Medicine" and "Insomnia from Diagnosis to Treatment". Results and discussion: We found evidence in the literature that sleep influences brain plasticity, spatial learning, motor training, long-term memory, Growth Hormone (GH) release, synapses remodelling and acts on the Hypothalamic-pituitary-adrenal (HPA) axis. Furthermore, clinical and practical findings also show that immunity is affected and children with sleep problems present significant disturbances in learning. Conclusion: The relationships between sleep reduction/ sleep disorders and daytime /nocturnal brain function, influence learning of school children.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".