Sleep problems over a year in sexually abused preschoolers
Bibliographic record
Abstract
The aim of the present study was to explore sleep problems in sexually abused preschoolers over a year. The sample consisted of 224 abused children and 83 nonabused children aged 3 to 6 years old at enrolment into the study (T1), and 85 abused children and 73 nonabused children at the second evaluation, approximately 1 year later (T2). Sleep problems were assessed using parental reports on the Child Behavior Checklist – Preschool Version. Multivariate analysis of covariance revealed that sexually abused preschoolers presented higher scores of sleep problems than nonabused children on all items related to sleep problems at T1. Results from a regression analysis revealed that sexual abuse remained associated with higher levels of sleep problems after controlling for sociodemographical variables. At T2, abused preschoolers still presented more sleep problems than nonabused children on all but one of the sleep items. Results from a repeated measure one-way analysis of covariance revealed that abused preschoolers presented higher total sleep scores at both measurement times. Time was not associated with levels of sleep problems and sleep problems did not evolve differently for abused and nonabused children. Sexually abused preschoolers are at risk of presenting with sleep problems. These results are worrisome given the negative impacts of sleep problems on child development. Research and clinical implications are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".