Matching treatment to develoment: emerging adults and substance-use disorder
Bibliographic record
Abstract
Emerging adults (age 18-25) drop out of substance use disorder (SUD) treatment earlier than those age 26+. Retention in treatment is important as it is correlated to long-term sobriety. There is a gap in the literature on how to improve retention in emerging adults. Through a systematic review and qualitative study, this thesis explored the best options to improve treatment retention in emerging adults with SUD. The systematic review summarized the literature and identified the highest treatment retention is reported to occur with contingency management, cognitive behavioral therapy, and opioid replacement therapy. In the qualitative study, health care professionals (HCPs) were interviewed regarding facilitators and barriers of treatment retention. Four themes were identified: 1) the emerging adults’ development, 2) their addiction and recovery, 3) the environment, and 4) SUD programing. Future recommendations include tailoring SUD programming to the developmental needs of emerging adults and involving HCPs in the design of SUD programming.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".