Gender Differences in Substance Use Across Marital Statuses
Bibliographic record
Abstract
Previous studies have noted that the relationship status of adults is substantially linked with levels of substance use. Understandably, the marital status of adults continues well beyond its initial phases, sometimes resulting in divorce, separation, or remarriage. This study seeks to extend our understanding of the linkages between marital status and substance use among adults. Using data from the 2012 National Survey on Drug Use and Health, we examine the substance use levels among a nationally representative sample of 14,715 adults. The analyses indicate that, for both females and males, marriage is, indeed, associated with lower levels of alcohol, cigarette, and marijuana use. Divorced individuals reported the highest levels of substance use. Interestingly, remarried individuals report higher levels of substance use than their counterparts in their first marriage, yet remarried men and women report lower levels of usage than do those who are currently divorced. Contextual and individual characteristics also yield several interesting patterns. In particular, distress and depression are shown to be much stronger predictors of substance use levels among divorced and remarried individuals. Divorced and remarried women, as compared to their male counterparts, are shown to be significantly more influenced by their employment status. The implications of this study are discussed, as are the potentially reciprocal nature of marital status and substance use.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".