Screening for substance use and mental health problems in a cross-sectoral sample of Canadian youth
Bibliographic record
Abstract
BACKGROUND: This project examines the substance use and mental health concerns of a cross-sectoral, national, service-seeking sample of adolescents and emerging adults using an extended version of the Global Appraisal of Individual Needs-Short Screener (GSS). It also aims to provide incremental evidence of the psychometric properties of the GSS. METHODS: A sample of 2313 youth aged 12-24 years who presented for service participated in the project. Youth were recruited from 89 participating services across Canada representing eight major clinical and non-clinical sectors. Participants completed the GSS and provided sociodemographic data. RESULTS: The majority of youth presenting for services endorsed concerns on the GSS and would be likely to meet diagnostic criteria for a disorder in a full diagnostic assessment according to the norms for the scale, while many endorsed multiple concerns. This was true in both clinical and non-clinical settings. Externalizing concerns and suicidality were significantly more common in younger participants, while substance use was significantly more common in older youth. Females were more likely to endorse internalizing and suicidality concerns, while males endorsed more substance use and crime/violence concerns. Internalizing and suicidality concerns were also more common in Canada's northerly regions. The reliability of the GSS was confirmed, however the factor structure revealed problems. CONCLUSIONS: Youth presenting across clinical and non-clinical service sectors endorse high levels of need, supporting the importance of universal, cross-sectoral screening. The GSS is a practical tool that service providers across sectors can employ to identify the addiction and mental health service needs of youth, although further psychometric work is warranted. Implications for screening and treatment in community contexts are discussed.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".