Socioeconomic Status Moderates the Impact of Emotional but not Physical Childhood Abuse on Women’s Sleep
Bibliographic record
Abstract
Abstract A recent systematic review highlighted associations between childhood abuse and adult sleep quality, and the need for research focused specifically on women and the role of moderating variables. The objectives of the present study were (1) to assess the impact of frequent physical and emotional child abuse on adult sleep among women; and (2) to assess the role that childhood socioeconomic status (SES) could play in moderating these associations. In-person data were collected from women living in a mid-sized city in western Canada in 2019–2020 (N = 185; M age = 40 years). Sleep quality was measured using the Pittsburgh Sleep Quality Index. Physical and emotional abuse experienced often or very often in childhood were assessed using single items (yes or no). Childhood SES was assessed by a single item and dichotomized at the sample median. Linear regression models examined associations between each form of abuse and continuous adult sleep quality score adjusted for covariates. Statistically significant interactions were stratified and examined by child SES group. Frequent physical and emotional childhood abuse were each associated with clinically and statistically significant increases in past-month sleep problem scores among women in adjusted models. This association was moderated by childhood SES for emotional child abuse, but not physical child abuse. Findings suggest that growing up in an upper-middle to upper SES household may buffer the adverse impact of frequent emotional child abuse on later adult sleep, but may not promote resilience in the context of frequent physical child abuse.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".