Health Risk Behaviors and Mental Health Problems as Mediators of the Relationship Between Childhood Abuse and Adult Health
Bibliographic record
Abstract
OBJECTIVES: We examined the relationship between childhood abuse and adult health risk behaviors, and we explored whether adult health risk behaviors or mental health problems mediated the relationship between childhood abuse and adult health problems and health care utilization. METHODS: We used logistic regression to analyze data from the Mental Health Supplement of the Ontario Health Survey, a representative population sample (N = 8116) of respondents aged 15 to 64 years. RESULTS: We found relationships between childhood sexual abuse and smoking (odds ratio [OR] = 1.52; 95% confidence interval [CI] = 1.16, 1.99), alcohol problems (OR = 2.44; 95% CI = 1.74, 3.44), obesity (OR = 1.61; 95% CI = 1.14, 2.27), having more than 1 sexual partner in the previous year (OR = 2.34; 95% CI = 1.44, 3.80), and mental health problems (OR = 2.26; 95% CI = 1.78. 2.87). We also found relationships between these factors (with the exception of obesity) and childhood physical abuse. Mediation analysis suggested that health risk behaviors and particularly mental health problems are partial mediators of the relationship between childhood abuse and adult health. CONCLUSIONS: Public health approaches that aim to decrease child abuse by supporting positive parent-child relationships, reducing the development of health risk behaviors, and addressing children's mental health are likely to improve long-term population health.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".