Gender Differences in Prevalence of Substance Use Disorders among Individuals with Lifetime Exposure to Substances: Results from a Large Representative Sample
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Research regarding substance use and substance use disorders (SUDs) shows significant gender differences in prevalence of substance use and dependence. Though lifetime exposure to substances is higher among males, previous reports have not regarded gender differences in prevalence of SUDs among individuals formerly exposed to substances. In addition, though substance abuse is particularly important when exploring gender differences, previous reports have largely focused on rates of transition to substance dependence alone. In this study, we explored gender differences in prevalence of SUDs among individuals with lifetime exposure to substances using a single diagnostic category (abuse or dependence). METHODS: We analyzed 11 different categories of substances: heroin, cocaine, cannabis, nicotine, alcohol, hallucinogens, inhalants, sedatives, tranquilizers, opioids, and amphetamines. Data were derived from the National Epidemiologic Survey on Alcohol and Related Conditions (Wave 1, n = 43,093). The impact of gender on prevalence of SUDs among individuals with lifetime exposure to substances was assessed with odds ratios (ORs) using logistic regressions and adjusted for socio-demographic factors. RESULTS: Our results show that among individuals with lifetime exposure to substances, males had a significantly higher prevalence of alcohol (OR = 2.95), sedatives (OR = 2.00), cannabis (OR = 1.93), tranquilizers (OR = 1.64), opioids (OR = 1.54), hallucinogens (OR = 1.31), and cocaine (OR = 1.26) use disorders compared with females. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Using a single broad diagnostic category highlights gender differences in the prevalence of SUDs among individuals with former exposure to substances. Specifically, the significant gender differences found for alcohol, sedatives, and cannabis use disorders may be important for tailoring preventive measures targeted at reducing rates of SUDs among males using these substances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".