The Relation between Childhood Adverse Experiences and Disability Due to Mental Health Problems in a Community Sample of Women
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the association between selected childhood adverse experiences and disability due to mental health problems in a community sample of women. Variables of interest included childhood physical and sexual abuse, parental psychiatric and substance abuse history, and sociodemographic factors. METHOD: Girls and women (aged 15 to 64 years) from a province-wide community sample (n = 4239) were asked about disability and most childhood adverse experiences through interview; a self-administered questionnaire inquired about child abuse. Logistic regression (crude and adjusted odds ratios) was used to test the associations between childhood adversity and disability due to mental health problems. RESULTS: Approximately 3% of the women had a disability due to mental health problems. Among women with a disability, about 50% had been abused while growing up. After controlling for income and age, we found that disability showed the strongest association with childhood sexual abuse, physical abuse, and parental psychiatric disorder. CONCLUSION: Disability due to mental health problems was experienced by women with and without exposure to abuse in childhood. However, childhood sexual abuse and physical abuse were important correlates of disability. Disability creates suffering and loss for the individual and society; this issue merits more research in relation to 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".