Identifying patterns of substance use and mental health concerns among adolescents in an outpatient mental health program using latent profile analysis
Bibliographic record
Abstract
Though mental health and substance use concerns often co-occur, few studies have characterized patterns of co-occurrence among adolescents in clinical settings. The current investigation identifies and characterizes these patterns among adolescents presenting to an outpatient mental health service in Ontario, Canada. Data come from cross-sectional standardized patient intake assessments from 916 adolescents attending an outpatient mental health program (January 2019-March 2021). Latent profile analysis identified patterns of substance use (alcohol, cannabis, (e-) cigarettes) and emotional and behavioral disorder symptoms. Sociodemographic and clinical correlates of these patterns were examined using multinomial regression. Three profiles were identified including: 1) low substance use and lower frequency and/or severity (relative to other patients in the sample) emotional and behavioral disorder symptoms (26.2%), 2) low substance use with higher emotional and behavioral disorder symptoms (48.2%), and 3) high in both (25.6%). Profiles differed in sociodemographic and clinical indicators related to age, gender, trauma, harm to self, harm to others, and service use. Experiences of trauma, suicide attempts, and thoughts of hurting others increased the odds of adolescents being in the profile high in both substance use and symptoms compared to other profiles. These findings further document the high rates of substance use in adolescents in mental health treatment and the profiles generally map onto three out of four quadrants in the adapted four-quadrant model of concurrent disorders, indicating the importance of assessing and addressing substance use in these settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".