Do internalising or externalising behaviours in adolescence mediate the child maltreatment‐alcohol substance use relationship?
Bibliographic record
Abstract
BACKGROUND AND AIMS: Child maltreatment (CM) is associated with several negative mental health outcomes in later life, including alcohol and substance use (AU and SU). Internalising (e.g. anxiety and depression) and externalising (e.g. delinquency and anti-social behaviour) behaviours have also been associated with CM, and with AU and SU. This study measured whether externalising or internalising behaviours in adolescence mediate the relationship between agency-reported CM and hospital admissions for AU or SU. DESIGN, SETTING AND PARTICIPANTS: Observational study linking administrative health data from Queensland, Australia, to prospective birth cohort data comprising agency-reported CM up to 14 years (exposure). MEASUREMENTS: Externalising and internalising behaviours at 14 years measured using the Youth Self-Report (mediator) and administratively linked inpatient hospital admissions for AU and SU from ages 25-39 (outcome). FINDINGS: Adjusted causal mediation analyses (n = 5092) found that externalising behaviours statistically significantly mediated 31% (P = 0.007) of the CM AU relationship, and 22% of the CM SU relationship (P = 0.016). Internalising behaviours did not statistically significantly mediate between CM and either AU or SU. CONCLUSIONS: In Queensland, Australia, externalising behaviours appear to partially mediate the relationship between agency-reported child maltreatment and hospital admissions for alcohol and substance use, while internalising behaviours do not.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".