Key characteristics and habits of the recreational opioid user
Bibliographic record
Abstract
OBJECTIVE: To identify key characteristics and habits of recreational opioid users. DESIGN: The data were compiled from volunteers who participated in clinical studies at a contract research organization in Toronto, Ontario, Canada. INTERVENTIONS: Data were collected from 5,018 male and female recreational opioid users via telephone and face-to-face screening interviews. Five recreational opioid users participated in a live interview broadcast on the internet. MAIN OUTCOME MEASURES: Demographic data, recreational drug use history, routes of recreational drug administration, alcohol use, and smoking status. A subset of the demographic information and recreational drug use history was summarized separately using data collected between 2013 and 2016 from 114 recreational opioid users who were not dependent on opioids. Interview excerpts were included from five recreational opioid users who described their real-world experiences with drug abuse, including the impact of abuse-deterrent opioid formulations on their drug abuse behavior. RESULTS: The preferred route of administration of opioids was oral (52 percent), followed by intranasal (36 percent), intravenous (10 percent), and buccal (chewing on a patch; 2 percent). Other substances used included nicotine, alcohol, and non-opioid psychoactive drugs (primarily cannabis). Oxycodone was the most frequently reported opioid of abuse. CONCLUSIONS: Recreational opioid users have distinct drug-related behaviors and preferences. Monitoring current trends and examining these behaviors is an important component to understand the potential safety risks associated with recreational opioid use.
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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".