Childhood Maltreatment and Substance Use Disorders among Men and Women in a Nationally Representative Sample
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".