Early alcohol onset: clinical and demographic characteristics of service-seeking youth.
Bibliographic record
Abstract
Background: Alcohol use in early adolescence is associated with increased health concerns and other negative consequences. Given the needs of this vulnerable population, it is critical to understand their risk factors and clinical characteristics. Objective: This cross-sectional study explores the clinical and demographic characteristics of service-seeking youth with and without early alcohol use onset. Method: 655 youth seeking services at a Canadian substance use and concurrent disorder service participated. Evaluations of mental health, substance use, demographic characteristics, and other risk factors were collected and compared among youth who reported an age of onset for alcohol use of under 14 years of age versus 14 years or older. Results: Youth who started using alcohol before age 14 were significantly more likely to report mental health difficulties, indicate use of a greater number of substances, and report experiencing more crime and violence problems. They also reported exposure to more types of trauma. Notably, more problems with crime and violence were significantly, and uniquely associated with an earlier age of alcohol use onset in a multivariate model. Conclusion: The present study identifies unique and clinically significant differences among youth who initiated alcohol use in early adolescence compared to later in adolescence. Stronger integrations between mental health and substance use services for youth with early alcohol use should be considered, given the vulnerability and concurrent difficulties they tend to face.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".