Child Maltreatment, Delayed Reward Discounting, and Alcohol and Other Drug Use Problems: The Moderating Role of Heart Rate Variability
Bibliographic record
Abstract
BACKGROUND: Child maltreatment (CM) is robustly associated with youth risk for addictive behaviors, and recent findings suggest that this may be mediated through impulsive discounting of future rewards. However, research indicates that youth self-regulation (emotional and cognitive), particularly in peer contexts, is critical to consider in the study of decision making. This study aimed to examine the indirect link between CM and alcohol and other drug use problems, through delayed reward discounting (DRD), among a community sample of emerging adults. Further, this investigation aimed to examine whether this indirect link was moderated by heart rate variability (HRV), a physiological proxy for regulation of stress reactivity. METHODS: = 2.24; 52.9% female) was assessed at 2 time points, with 1 year between assessments. The sample was comprised of rural emerging adults from lower socioeconomic backgrounds. DRD was examined using a monetary choice task, and HRV reactivity was derived during a social stress task. RESULTS: Increased CM experiences were significantly linked to riskier DRD. HRV reactivity amplified the indirect effect between CM and alcohol use problems via riskier DRD. CONCLUSIONS: The results demonstrate that the connection between CM and alcohol use problems via impulsive decision making is modulated by acute stress response reactivity, as indexed by HRV.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".