Iterative development and clinical outcomes of an outpatient young adult substance use program.
Bibliographic record
Abstract
OBJECTIVE: Substance use problems peak in emerging adulthood and often co-occur with other psychiatric disorders. Developmentally tailored services are critical to reduce harms, promote recovery, and prevent persistence or exacerbation. The Young Adult Substance Use Program is an evidence-informed outpatient program for 17- to 25-year-olds that aligns with recent frameworks and principles for the treatment of substance use disorders among youth. This article provides (1) an overview of the program's evolution and (2) an evaluation of (a) recruitment, retention, and engagement; (b) clinical characteristics; and (c) treatment outcomes. METHOD: Data come from the Young Adult Substance Use Program measurement-based care assessments and clinical chart reviews. A series of descriptive statistics and multilevel linear regressions were performed. RESULTS: s < .001) and clinically important (per minimal clinically important differences) changes were present for substance use, depression, anxiety, posttraumatic stress disorder symptoms, and quality of life. Approximately 80% reported a clinically important improvement by ∼12 weeks, although persistent clinical elevations were nonetheless present. CONCLUSIONS: Overall, the Young Adult Substance Use Program is an example of an effective evidence-informed developmentally tailored and iteratively refined pragmatic outpatient young adult substance use program. Challenges, lessons learned, and future directions are discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".