Do green spaces protect night sleep duration and sleep quality in children and adolescents? Evidence from 10 observational studies
Bibliographic record
Abstract
BACKGROUND: More than half of children and adolescents do not get the recommended eight hours of sleep necessary for optimal growth and development. Since sleep was very important for the growth and development of children and adolescents, it is necessary to improve the environment for children and adolescents to sleep. Green space have been found to improve sleep in general, and the aim of this systematic review is to assess the association of green space on sleep among children and adolescents. METHODS: The PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database and Wan Fang database were searched from the establishment of these databases to July 31, 2024. Newcastle - Ottawa Scale (NOS) and Joanna Briggs Institute (JBI) scale were applied to evaluate the quality of the included studies. Stata17.0 was used to draw forest maps and sensitivity analysis was performed on the results of meta-analysis to evaluate the stability of the results. Publication bias was assessed using funnel plots and Egger test method. RESULTS: A total of 10 articles with 102,873 participants meeting the criteria were included in this study, including 6 articles were cohort studies and 4 articles were cross-sectional studies. The sample sizes ranged from 328 to 63,352 participants per study. The results show that green space have a positive association on night sleep duration in children and adolescents (OR: 0.969, 95%CI [0.869, 1.079] and OR: 0.949, 95%CI [0.854, 1.055]). CONCLUSIONS: The results show that green space have a positive association on night sleep duration in children and adolescents. The association of green space on sleep quality and napping could be further studied in the future.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".