Nonmedical use of opioid analgesics among Ontario students.
Bibliographic record
Abstract
OBJECTIVE: To explore the prevalence and the demographic predictors of nonmedical use of opioid analgesics in the Canadian adolescent population. DESIGN: Data are based on self-reports derived from the 2007 Ontario Student Drug Use and Health Survey, which is an anonymous, in-school, cross-sectional survey. SETTING: Schools in Ontario. PARTICIPANTS: A total of 2914 students in grades 7 to 12. MAIN OUTCOME MEASURES: Demographic predictors of nonmedical use of opioid analgesics during the past year and the sources of opioid analgesics. RESULTS: Students ranged in age from 12 to 19 years (mean 15.0, SD 1.9) and 52% were male. Of the students surveyed, 20.6% (95% confidence interval [CI] 18.9% to 22.3%) reported using opioid analgesics at least once nonmedically during the past year, with 6.2% using exclusively nonmedically and 14.4% using nonmedically and medically. Female students (16.6%, CI 14.1% to 19.6%) were more likely than male students (12.0%, CI 10.0% to 14.2%) to have used opioid analgesics both nonmedically and medically in the past year, although exclusive nonmedical use was similar between female (6.7%, CI 5.3% to 8.5%) and male (5.8%, CI 4.5% to 7.3%) students. Among students who reported using opioid analgesics nonmedically, 72% reported obtaining them from home and only 6% reported obtaining them from friends. Nonmedical opioid analgesic users had higher past-year prevalences for alcohol use, daily smoking, and other illicit drug use compared with nonusers. CONCLUSION: Nonmedical use of opioids is common among Ontario students. The motivation for using these medications without prescriptions or without medical supervision is not known. Students might have used these medications recreationally or for pain relief. Regardless of motivation, these medications are being used without medical supervision. It is important to note that the home is the main source for opioid analgesics in the absence of a prescription. Parents should be vigilant and educate themselves and their children about these medications, ensuring that prescription opioids are stored properly and avoiding casual sharing of these medications among family members.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".