Motives for non-medical prescription opioid (NMPO) use among young people in a semi-rural Canadian Province
Bibliographic record
Abstract
Objective In Canada, young people aged 15–24 had the fastest growing rates of hospitalization for opioid poisoning in the last decade, compared to other age groups. This cross-sectional study examined non-medical prescription opioid (NMPO) use in youth and young adults in a semi-rural Canadian province.Method Participants completed an online survey about motives for NMPO use, and knowledge and utilization of local resources.Results All participants (N = 108) were self-reported opioid users between the ages of 15–25 years. The majority of participants had been prescribed an opioid by a physician in the past. Regression analysis showed that being older, having an opioid prescription, and using opioids for pain, coping, or enhancement reasons predicted higher levels of disordered opioid use. Pain was the most common motive for NMPO use and the strongest predictor of disordered opioid use. Most participants reported having limited knowledge about harm reduction resources in their communities.Conclusions Although the opioid crisis is a wide-spread concern, understanding why youth and young adults engage in NMPO use in local contexts may facilitate the development and implementation of resources that are more useful for these individuals, which could be scalable to other regions across Canada and internationally.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".