Childhood maltreatment as a predictor of substance use/misuse among youth: A systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to comprehensively describe whether experiencing a variety of childhood maltreatment types predicts a variety of substance use/misuse types among youth, beyond the narrow scope covered in previous systematic reviews on similar topics. A literature search was conducted in June, 2022 using PubMed, PsycInfo, and Embase. 58 studies (total participant n=170,749) were included. These studies were primarily organized by substance type outcomes including alcohol (n=43), cannabis (n=25), unspecified substances (n=25), and other specific substances (n=10). Results were further stratified by maltreatment type. For specific maltreatment and substance type combinations, the majority of studies indicated that childhood maltreatment was a significant predictor of substance use/misuse in youth. Of the 10 meta-analyses we conducted, significant associations were found for the majority (9/10) of maltreatment and substance type combinations. For instance, unspecified childhood maltreatment increased the probability of youth alcohol use by about four times, which was the highest relative risk found. In conclusion, this study shows that childhood maltreatment is a predictor of youth substance use/misuse.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".