Pathways Linking Adverse Childhood Experiences to Cigarette Smoking Among Young Black Men: a Prospective Analysis of the Role of Sleep Problems and Delayed Reward Discounting
Bibliographic record
Abstract
BACKGROUND: African American men experience increases in smoking during the young adult transition. Exposure to childhood adversity, a risk factor which disproportionately affects African American men, has been identified as a robust precursor to health risk behavior in general and cigarette smoking in particular. The intermediate mechanisms that transmit the influence of early adversity to smoking behavior are not well understood. PURPOSE: We tested a model of the escalation of smoking behaviors among young adult African American men, investigating sleep disturbance and delayed reward discounting as intermediate factors linking adverse childhood experiences with smoking. METHODS: = 23.02) from 505 African American men living in rural counties in South Georgia. Men provided self-report data on their adverse childhood experiences, sleep problems, and smoking behavior using audio-assisted computer self-interviews. Men also completed a computer-based delayed reward discounting task. RESULTS: Structural equation modeling analyses supported our hypotheses: Adverse childhood experiences predicted poor sleep adequacy, which forecast increases in delayed reward discounting; discounting, in turn, predicted increased smoking. Significant indirect pathways were detected linking adversity to discounting via sleep adequacy and linking sleep adequacy to smoking via discounting. CONCLUSIONS: Prevention and intervention researchers can draw on these findings to develop programs that focus on sleep adequacy to reduce smoking in African American men exposed to childhood adversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".