Prevalence and key covariates of non‐medical prescription opioid use among the general secondary student and adult populations in <scp>O</scp>ntario, <scp>C</scp>anada
Bibliographic record
Abstract
INTRODUCTION AND AIMS: To assess the prevalence and key covariates of non-medical prescription opioid use (NMPOU) in two representative surveys of adults (Centre for Addiction and Mental Health Monitor, CM) and secondary-school students (Ontario Student Drug Use and Health Survey, OSDUHS). DESIGN AND METHODS: Data from the 2010 and 2011 cycles (n = 4023) of CM--a stratified, multi-stage, random-digit-dialling telephone survey of adults (18 years and older)--and the 2011 cycle of OSDUHS (n = 3266)--a self-administered written questionnaire-based survey of grade 7-12 public system students--were used. Besides NMPOU prevalence, associations were assessed by univariate and multi-step multivariate (logistic regression) analyses. NMPOU and key socioeconomic (i.e. sex, age, Aboriginal ethnicity, household location, income, subjective social status), health indicators (physical health status, psychological distress, suicidal ideation), drug use (cigarette smoking, binge drinking, cannabis use, other drug use) were measured. RESULTS: NMPOU (past year) prevalence was 15.5% in students and 5.9% in adults. Various univariate associations with social, health and drug use factors were found in both populations, with differences by sex. Based on multivariate analyses, other drug use (male students) and rural residence, subjective social status, other drug use and suicidal ideation (female students); marital status and cannabis use (male adults) and binge drinking (female adults) were independently associated with NMPOU in the respective study populations. DISCUSSION AND CONCLUSIONS: NMPOU was high in adults and especially students. Independent predictors of NMPOU were largely inconsistent by sex. Notably, NMPOU is widely distributed across socio-demographic and -economic strata, and thus requires broad-based interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".