Non-Medical Prescription Opioid Use among High-School Adolescents in Atlantic Canada
Bibliographic record
Abstract
Non-Medical Prescription Opioid (NMPO) use is one of the most prevalent forms of substance use among Canadian adolescents. Despite the evidence of NMPO use as a major public health concern, we have major gaps in our understanding of the correlates that shape NMPO use patterns. Our study addresses the gaps by: 1- describing the sociodemographic, substance use, and psychosocial characteristics of Atlantic Canada high-school student NMPO users, and examining whether frequency of use is differentially shaped by these measures; 2- examining the substance use patterns by which NMPOs are used, and whether these patterns are associated with psychosocial outcomes, particularly mental health (depression, suicidality, and anxiety) and protective (school connectedness and parental monitoring) factors. We analyzed data derived from the 2012 cycle of the Student Drug Use Survey in the Atlantic Provinces using descriptive statistics and regression models. Our results indicate that frequent and infrequent NMPO users carry the same mental health burden, are similarly affected by protective factors, and are similarly likely to engage in other substance use. NMPO use is robustly associated with medical opioid use, with one-third of medical users engaging in misuse. Subgroups of NMPO users (based on patterns of additional substance use) share a similar burden of mental health problems; however, a strong negative association between greater parental monitoring and any additional substance use is evident.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".