Prevalence and correlates of intentional substance use to reduce illicit opioid use in a Canadian setting
Bibliographic record
Abstract
Background: While preliminary evidence has begun to document intentional use of one substance to reduce the use of another, the phenomenon of drug substitution among people who use illicit opioids remains understudied. Therefore, we sought to estimate the prevalence and correlates of intentional substance use to reduce illicit opioid use among persons who use drugs (PWUD). Methods: We analysed data from three prospective cohorts of PWUD in Vancouver, Canada, using multivariable generalized estimating equations (GEE). Results: Between June 2012 and June 2016, 1527 participants were recruited and contributed 4991 interviews. Of those, 336 (22%) illicit opioid-using participants self-reported substitution to reduce illicit opioid use at least once during study period contributing 467 (9.4%) interviews. Among those interviews, substances substituted for opioids were alcohol (15 participants, 3.2%), stimulants (235, 50.3%), cannabis (129, 27.6%), benzodiazepines (21, 4.5%), and others (20, 4.3%). In multivariable GEE model adjusted for socio-demographic factors, reporting substitution to reduce illicit opioid use was positively associated with greater likelihood of daily cannabis use (Adjusted Odds Ratio = 1.56, 95% Confidence Interval: 1.24-1.96]. Conclusions: While daily cannabis use was associated with reporting opioid substitution attempts, additional study is needed to examine potential of cannabis/cannabinoids to reduce illicit 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".