A Preliminary Examination of a Strengths-Based Treatment for Adolescent Substance Use Issues
Bibliographic record
Abstract
Adolescent substance use disorders are a major public health concern. Given the many challenges associated with treating this population, ongoing research in this area is imperative. The purpose of the current study was to provide a preliminary examination of the substance use outcomes associated with an adolescent residential treatment program that utilizes a strengths-based approach. The current study examined treatment outcomes in 61 adolescents (aged 14 to 18 years) who completed a 5-week strengths-based residential treatment program for adolescent substance use issues. Results showed significant reductions in frequency of alcohol and marijuana use from pretreatment to 3 and 6 months posttreatment, and in opioid use frequency from pretreatment to 3 months posttreatment. In addition, changes in self-reported substance use goal progress scores indicated significant improvements in goal progress from pretreatment to 3 months posttreatment; these improvements were maintained at 6 months posttreatment. Finally, depressive symptomology was also found to decrease significantly from pretreatment to posttreatment, and this decrease was found to be predictive of better substance use outcomes at 6 months posttreatment. These findings add to the literature by providing preliminary data that support the utility of the strengths-based approach in the treatment of adolescence substance use issues.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".