Predictors of problematic adult alcohol, cannabis, and other substance use: A longitudinal study of two samples
Bibliographic record
Abstract
Abstract This study examined whether a key set of adolescent and early adulthood risk factors predicts problematic alcohol, cannabis, and other substance use in established adulthood. Two independent samples from the Child Development Project (CDP; n = 585; 48% girls; 81% White, 17% Black, 2% other race/ethnicity) and Fast Track (FT; n = 463; 45% girls; 52% White, 43% Black, 5% other race/ethnicity) were recruited in childhood and followed through age 34 (CDP) or 32 (FT). Predictors of substance use were assessed in adolescence based on adolescent and parent reports and in early adulthood based on adult self-reports. Adults reported their own problematic substance use in established adulthood. In both samples, more risk factors from adolescence and early adulthood predicted problematic alcohol use in established adulthood (compared to problematic cannabis use and other substance use). Externalizing behaviors and prior substance use in early adulthood were consistent predictors of problematic alcohol and cannabis misuse in established adulthood across samples; other predictors were specific to the sample and type of substance misuse. Prevention efforts might benefit from tailoring to address risk factors for specific substances, but prioritizing prevention of externalizing behaviors holds promise for preventing both alcohol and cannabis misuse in established adulthood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".