Explaining child maltreatment and aggression among Chinese drug user: The mediating and moderating roles of drug craving and impulsivity
Bibliographic record
Abstract
BACKGROUND: The cycle of violence highlights a strong correlation between child maltreatment and aggression. However, there remains a significant gap in the pathway models of the cycle of violence. Given the exceptionally high rates of child maltreatment and violent crime among Chinese drug users, it is essential to examine the mechanisms of the cycle of violence within this group. OBJECTIVE: The current study incorporates drug craving and impulsivity into the child maltreatment-aggression mechanism. We explore the potential mediating and moderating roles of these variables and further examine the heterogeneity. PARTICIPANTS AND SETTING: = 8.38) were recruited as the final sample. METHODS: We employed moderated mediation and serial mediation models to explore the roles of drug craving and impulsivity. The Johnson-Neyman method was utilized to investigate moderating effects. Rich demographic variables and depression were controlled. RESULTS: There was no direct relationship between child maltreatment and aggression. The moderated mediation model indicated that drug craving played a mediating role, and there was a substitutive relationship between impulsivity and drug craving. The serial mediation model showed that child maltreatment could only affect drug craving (not impulsivity) and could ultimately influence aggression through a chain relationship. Heterogeneity tests revealed that the mechanisms might differ among various types of maltreatment. CONCLUSION: Drug craving holds a significant position in the cycle of violence. Compared to impulsivity, it is a more proximal factor to child mistreatment. Future research should also focus on the heterogeneity of child maltreatment for targeted interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".