A Trio of Risk Factors for Childhood Sexual Abuse: Investigating Exposure to Parental Domestic Violence, Parental Addiction, and Parental Mental Illness as Correlates of Childhood Sexual Abuse
Bibliographic record
Abstract
Targeted screening for sexual abuse is needed for social workers to accurately identify those at risk. Drawing on a cumulative disadvantage framework, this study investigates how parental addictions, parental mental illness, and exposure to domestic violence, both individually and cumulatively, are associated with childhood sexual abuse (CSA). Two waves of regionally representative data were analyzed. Bivariate and logistic regression analyses were conducted using the 2010 Brief Risk Factor Surveillance Survey (BRFSS) (n = 9,241 men, n = 13,627 women) and replicated using the 2012 BRFSS (n = 11,656 men, n = 18,145 women). The 2010 data indicated that 8.5 percent of men who had endured all three childhood adversities reported that they had experienced CSA, compared with 0.6 percent of men who did not experience any of these adversities. Levels of CSA for women in 2010 were 28.7 percent for those experiencing all three risk indicators, and 2.1 percent for women with no risk indicators. Results were similar in the BRFSS 2012. Those with two or more risk factors had between five- and eightfold higher odds of CSA. Social workers may be able to decrease false positives if they screen for CSA based on the presence of two or more risk factors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".