Understanding the service needs of youth with opioid use: A descriptive study of demographics and co-occurring substance use and mental health concerns
Bibliographic record
Abstract
INTRODUCTION: Opioid misuse among youth is a significant concern given its potential negative consequences; yet youth who use opioids continue to face challenges in seeking treatment. The goal of this study was to understand the demographics, mental health, and substance use and co-occurring concerns of service-seeking youth with opioid use to inform future services and research. METHODS: This study characterized 1769 youth between 14 and 24 years old seeking services at an urban treatment facility in Toronto, Ontario (ON) between 2008 and 2019. Youth who could adequately read English, had no immediate psychiatric service needs, provided research consent, and responded to a question about opioid use history were eligible. Participants provided information on demographics, substance use, mental health, and treatment motivation. RESULTS: Youth who use opioids were more likely to report challenges with social determinants of health, use more substances, initiate substance use earlier, and use some substances at a higher frequency than youth who do not use opioids. Youth with opioid use reported more co-occurring mental health and substance use concerns than youth with no opioid use. They also endorsed more trauma and a likely PTSD diagnosis. Not being in employment, education, or training and using a higher number of substances positively predicted youth opioid use. In terms of treatment motivation, youth who use opioids endorsed more internal forms of motivation than youth who do not use opioids. CONCLUSIONS: Youth who use opioids have complex social and co-occurring mental health and substance use needs that may be addressed with integrated community-based services. Future research should focus on developing evidence-based opioid treatment programs in this context and other innovative platforms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".