Changes in child abuse experience associated to sleep quality: results of the Korean Children & Youth Panel Survey
Bibliographic record
Abstract
BACKGROUND: A victim of child abuse can often develop mental illness. The early detection of mental illness of children could be supported by observing sleep quality. Therefore, we examined the relationship between sleep quality and the changes in child abuse by the child's own parents over the study period. METHODS: Data from the 2011-2013 Korean Children and Youth Panel Survey was used, and 2012 was set as the baseline. Adolescents who had poor sleep quality in 2011 were excluded from the analysis to obtain the final study population of 1276 adolescents aged 14 and 15 years. The generalized estimating equation model (GEE) was used for statistical analysis. RESULTS: Children who had experienced and/or were currently experiencing child abuse showed significantly poorer sleep quality (current year abuse only: odds ratio [OR] = 0.57, 95% confidence interval [CI] = 0.41, 0.79; prior year abuse only: OR = 0.72, 95% CI = 0.52, 0.99; continuous abuse: OR = 0.56, 95% CI = 0.39, 0.80) compared to children who had no experience of child abuse. CONCLUSION: Child abuse remains a traumatic experience that influences the quality of sleep and hinders the child's proper psychological development. We suggest approaching this issue at both the community and national levels to protect the victims.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".