Uncovering Hidden Risk Profile Phenotypes of Substance Use Among Canadian Adolescents via Cluster Analysis of COMPASS Data
Bibliographic record
Abstract
Abstract This study utilized a longitudinal health survey to identify sub-phenotypes of behavioral risk profiles related to youth substance use. We analyzed 8824 Canadian secondary school students who completed COMPASS questionnaires in 2016/17 and were followed up until 2018/19. The fuzzy clustering algorithm was used to identify four sub-phenotypes of risk profiles, including low-risk, medium-low-risk, medium-high-risk, and high-risk subgroups. The study found that the mean scores of health risk behaviors within each subgroup increased across the three waves, indicating a rising risk of health behaviors over time. The results confirm heterogeneity in the prevalence and characteristics across the subgroups. These findings can help school program managers and policymakers develop targeted interventions to reduce substance use among at-risk individuals and improve youth health behaviors. By identifying at-risk groups, stakeholders can develop effective preventive measures against addictive behaviors in school settings. The study results offer evidence to support the development of risk reduction interventions for youth substance use and ultimately improve youth health behaviors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".