The influence of substance use on depressive symptoms among young adult black men: The sensitizing effect of early adversity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Depressive symptoms have been identified as an important consequence of substance use. Both heavy drinking and marijuana use have acute and short-term effects on systems that regulate emotion, increasing the potential for substance use to induce problems with negative affect and irritability. We investigated the effects of alcohol and marijuana use on depressive symptoms among a sample of young Black men. We also tested the stress sensitization hypothesis that exposure to adverse childhood experiences would amplify the influence of substance use on men's depressive symptoms. METHODS: Hypotheses were tested with 505 rural Black men who, at ages 19-22 years, provided data on their substance use, adverse childhood experiences, and depressive symptoms; they provided data again 18 months later. RESULTS: Substance use forecasted increases in depressive symptoms; cross-lagged analyses yielded no evidence for the inverse path, depressive symptoms increasing substance use. The impact of substance use on depressive symptoms was amplified among young Black men who were exposed to adverse childhood experiences. Substance use did not significantly predict depressive symptoms when adversity was low. DISCUSSION AND CONCLUSIONS: Our findings suggest that, during young adulthood, substance use increases depressive symptoms among Black men who were exposed to childhood adversity. Because childhood adversity disproportionately affects Black men, these findings inform future cross-group research designed to investigate racial disparities in the consequences of substance use. SCIENTIFIC SIGNIFICANCE: Depressive symptoms may be understood as an effect as well as a cause of substance use, particularly among vulnerable young Black men. (Am J Addict 2017;26:400-406).
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".