Prevalence and characteristics of sleep problems of Indonesian children in 0 – 36 months old
Bibliographic record
Abstract
Background: A quarter of child population experiences sleep problems in their first three years of life. Inadequacy and problems of sleep for children may be caused by various causes that impact their mental health, emotional states, physical states, and immune systems. This also may culminate to behavioural problems. Objective: The aim of this study is to identify the prevalence of sleep problems in 0–36 months old Indonesian children. Methods: A cross-sectional study was conducted in Tulungagung, East Java, Indonesia. Children aged 0–36 months old were enrolled by using a quota sampling. Brief Infant Sleep Questionnaire (BISQ) was used in this study to assess the sleep problems. All obtained data were presented as a distribution and percentage of each variable referring to the BISQ indicators. Results: A total of 493 children were enrolled in this study. This study found that there were 153 children (31%) who had experienced sleep problems, 79 children (16%) who had nocturnal sleep duration less than 9 hours, 62 children (12,8%) who had nocturnal waking more than 3 times, and 20 children (4%) who had duration of wakefulness during the night more than 1 hour respectively. Conclusion: Although majority of parents thought that there were no sleeping problems with their children, the prevalence of sleep problems in 0–36 months old Indonesian children was quite high (31%), suggesting low parental awareness towards sleep problems of their children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".