Non-medical use of prescription opioids: use to experience subjective effects vs. other non-medical use among adults in Ontario, Canada from 2020 to 2024
Bibliographic record
Abstract
OBJECTIVE: The opioid crisis remains an important public health concern, with nonmedical use of prescription opioids (NMUPO) playing a significant role. However, limited evidence exists on how adults engaging in NMUPO for subjective effects differ from those who use them for other nonmedical reasons. This study aims to identify and examine factors associated with engagement in NMUPO for subjective effects. METHOD: = 7,655). The surveys used a Qualtrics-based web survey to assess NMUPO, sociodemographic factors, substance use, and mental health. Data were analyzed using multivariate multinomial logistic regression. RESULTS: About 3% of adults engaged in NMUPO for subjective effects/to get high, and 15% of participants engaged in NMUPO for other nonmedical purposes during 2020 and 2024. These percentages remained stable over the years. The risk of NMUPO for subjective effects, compared with NMUPO for other purposes, was significantly higher among Asian people compared with their White counterparts (relative risk ratio [RRR] = 1.80, 95% CI [1.08, 3.01]) and among those with children (RRR = 2.53, 95% CI [1.64, 3.92]). Similarly, individuals with low household income, current other substance use, and psychological distress exhibited a higher risk of NMUPO, after adjusting for covariates. CONCLUSIONS: Individuals who use prescription opioids nonmedically for subjective effects or other reasons differ by race/ethnicity, parental status, income, substance use, and level of psychological distress. These findings suggest the need for targeted prevention and intervention strategies to address the unique needs and behaviors of different user groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".