Initial Insights from a Quality Improvement Initiative to Develop an Evidence-informed Young Adult Substance Use Program
Bibliographic record
Abstract
High rates of substance misuse during emerging adulthood (~17-25 years of age, also referred to as young adulthood) require developmentally appropriate clinical programs. This article outlines: 1) the development of an evidence-informed young adult outpatient substance use program that takes a biopsychosocial patient-centred approach to care; 2) a quality improvement process and protocol; and 3) the patient characteristics of an initial cohort. Literature reviews, program reviews, environmental scans, and consultations with interested parties (including individuals with lived expertise) were used to develop the program. A 12-week measurement-based care program was developed comprising: 1) individual measurement-based care and motivational enhancement therapy sessions; 2) group programming focused on cognitive behavioural therapy, mindfulness, distress tolerance, and emotional regulation; 3) clinical consultations for diagnostic clarification and/or medication review; and 4) an independent Community Reinforcement Approach Family Training (CRAFT) group for loved ones. A measurement system was concurrently created to collect clinical and program evaluation data at six time points. In the first 21 months of the program, 152 young adults enrolled in the program (mean age = 21 years old, 47% female gender) primarily reporting treatment targets of cannabis (68%) and alcohol (63%) and almost all presenting with co-occurring mental health concerns (95%). The initial cohort who completed the program showed symptom improvements. Collectively, the program demonstrates the feasibility of developing an evidence-informed young adult substance use program using measurement-based care, but also the need for flexibility and ongoing monitoring to meet local needs.
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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 | 0.064 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".