Childhood Maltreatment and Substance Use Disorders among Men and Women in a Nationally Representative Sample
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To examine the association between a history of 5 types of childhood maltreatment (that is, physical abuse, sexual abuse, emotional abuse, physical neglect, and emotional neglect) and several substance use disorders (SUDs), including alcohol, sedatives, tranquilizers, opioids, amphetamines, cannabis, cocaine, hallucinogens, heroin, and nicotine, in a nationally representative US adult sex-stratified sample. METHOD: Data were drawn from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC), a nationally representative US sample of adults aged 20 years and older (n = 34 653). Logistic regression models were conducted to understand the relations between 5 types of childhood maltreatment and SUDs separately among men and women after adjusting for sociodemographic variables and Diagnostic and Statistical Manual of Mental Disorders (DSM) Axis I and II mental disorders. RESULTS: All 5 types of childhood maltreatment were associated with increased odds of all individual SUDs among men and women after adjusting for sociodemographic variables, with the exception of physical neglect and heroin abuse or dependence, emotional neglect, and amphetamines and cocaine abuse or dependence among men (adjusted odds ratio range 1.3 to 4.7). After further adjustment for other DSM Axis I and II mental disorders, the relations between childhood maltreatment and SUDs were attenuated, but many remained statistically significant. Differences in the patterns of findings were noted for men and women for sexual abuse and emotional neglect. CONCLUSIONS: This research provides evidence of the robust nature of the relations between many types of childhood maltreatment and many individual SUDs. The prevention of childhood maltreatment may help to reduce SUDs in the general population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it