Seeking prescription opioids from physicians for nonmedical use among people who inject drugs in a Canadian setting
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite the high prevalence of prescription opioid (PO) misuse, little is known about the phenomenon of seeking POs for nonmedical use among high-risk populations, such as people who inject drugs (PWID). We therefore sought to examine the prevalence and correlates of seeking POs from a physician for nonmedical use among PWID in Vancouver, Canada. METHODS: Cross-sectional data from two open prospective cohort studies of PWID in Vancouver were collected between June 2013 and May 2014 (n = 1252). Multivariable logistic regression was used to identify factors associated with seeking POs from physicians for nonmedical use. RESULTS: Of 1252 participants, 458 individuals (36.6%) reported ever trying to get a PO prescription from a physician for nonmedical use and, of these, 343 (74.9%, comprising 27.4% of the total sample) reported ever being successful. Variables independently and positively associated with PO-seeking behavior included older age (adjusted odds ratio [AOR] = 1.02), Caucasian ethnicity (AOR = 1.38), having ever overdosed (AOR = 1.32), having ever participated in methadone maintenance therapy (AOR = 1.90), having ever dealt drugs (AOR = 1.65), and having ever been refused a prescription for pain medication (AOR = 2.02) (all p < .05). DISCUSSION AND CONCLUSIONS: We observed that PO-seeking behavior was common among this sample of PWID and associated with several markers of higher intensity drug use. SCIENTIFIC SIGNIFICANCE: Our findings highlight the need to identify evidence-based public health and clinical strategies to mitigate PO misuse among PWID without compromising care for PWID with legitimate medical concerns. (Am J Addict 2016;25:275-282).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".