Illicit Opioid Use and its Key Characteristics: A Select Overview and Evidence from a Canadian Multisite Cohort of Illicit Opioid Users (OPICAN)
Bibliographic record
Abstract
OBJECTIVES: To summarize key characteristics and consequences of illicit opioid use from the literature and to present corresponding data from a multisite sample of illicit opioid users in 5 Canadian cities (OPICAN study). METHOD: We undertook an overview of recent literature from North America, Australia, and Europe. We obtained data from the multicity OPICAN cohort study, which consisted of an interviewer-administered questionnaire, a standardized mental health instrument (the Composite International Diagnostic Interview Short Form for depression), and saliva-antibody tests for infectious disease (that is, HIV and hepatitis C virus). The baseline sample (n=679) was collected in 2002. RESULTS: Illicit opioid use in Canada and elsewhere is becoming increasingly heterogeneous in terms of opioid drugs used, with heroin playing an increasingly minor role; further, it predominantly occurs in a context of polydrug use (for example, cocaine-crack or benzodiazepines). Large proportions of illicit opioid users have physical and (or) mental health comorbidities, including infectious disease and (or) depression, and therefore require integrated interventions. Finally, morbidity risks among illicit opioid users are often predicted by social marginalization factors, for example, housing status or involvement in CONCLUSIONS: Given the epidemiologic profile and high disease burden associated with contemporary forms of illicit opioid use, more effective treatment approaches are urgently needed in Canada and elsewhere. Specifically, treatment must adjust to the extensive polysubstance use realities, yet it must also more effectively address the complex physical and (or) mental health comorbidities presented by this high-risk population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".