Predictors of Alcohol Use Disorders Among Young Adults: A Systematic Review of Longitudinal Studies
Bibliographic record
Abstract
AIMS: Alcohol use disorders (AUDs) are highly disabling neuropsychiatric conditions. Although evidence suggests a high burden of AUDs in young adults, few studies have investigated their life course predictors. It is crucial to assess factors that may influence these disorders from early life through adolescence to deter AUDs in early adulthood by tailoring prevention and intervention strategies. This review aims to assess temporal links between childhood and adolescent predictors of clinically diagnosed AUDs in young adults. METHODS: We systematically searched PubMed, Scopus, PsycINFO and Embase databases for longitudinally assessed predictors of AUDs in young adults. Data were extracted and assessed for quality using the Newcastle-Ottawa quality assessment tool for cohort studies. We performed our analysis by grouping predictors under six main domains. RESULTS AND CONCLUSION: Twenty two studies met the eligibility criteria. The outcome in all studies was measured according to the Diagnostic Statistical Manual of Mental Disorders. Our review suggests strong links between externalizing symptoms in adolescence and AUDs in young adulthood, as well as when externalizing symptoms co-occur with illicit drug use. Findings on the role of internalizing symptoms and early drinking onset were inconclusive. Environmental factors were influential but changed over time. In earlier years, maternal drinking predicted early adult AUD while parental monitoring and school engagement were protective. Both peer and parental influences waned in adulthood. Further high-quality large longitudinal studies that identify distinctive developmental pathways on the aetiology of AUDs and assess the role of early internalizing symptoms and early drinking onset are warranted.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".