Self-reports of prescription opioid abuse and diversion among recreational opioid users in a Canadian and a United States city
Bibliographic record
Abstract
OBJECTIVE: To explore behaviors related to prescription opioid abuse and diversion in individuals who self-reported past recreational (nonmedical) opioid use. DESIGN: A questionnaire was developed and included in two abuse potential clinical studies conducted in Canada (Toronto, ON, August 2010 to January, 2011) and the United States (Salt Lake City, UT, February-May 2011). PARTICIPANTS: Recreational opioid users. MAIN OUTCOME MEASURE(S): Self-reported behaviors related to prescription opioid abuse and diversion. RESULTS: The questionnaire was completed by 174 participants in the Canadian study and 80 participants in the US study. Most participants reported that they used prescription opioids for nonmedical purposes a few times a month. Most had taken their first prescription opioid between the ages of 12 and 24 years and the two most common reasons were to treat pain or to feel high/stoned. When asked about specific opioids taken for nonmedical purposes in the past year, oxycodone, acetaminophen with codeine, and morphine were commonly used by both cohorts, whereas hydrocodone use was substantially greater in the US cohort versus the Canadian cohort. Participants reported various tampering methods and routes of administration, with swallowed whole, crushed and snorted, and chewed/crushed and swallowed as the most prevalent. Most participants indicated taking other drugs with prescription opioids to get high, most commonly marijuana and alcohol. The most common sources for obtaining prescription opioids were family/friends. CONCLUSIONS: Two cohorts of recreational opioid users from Canada and the United States reported similar experiences with various prescription opioids and indicated a predominance of diversion from family/friends.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 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".