The association between cannabis and codeine use: a nationally representative cross-sectional study in Canada
Bibliographic record
Abstract
BACKGROUND: Due to the growing use of cannabis for the purposes of pain relief, evidence is needed on the impact of cannabis use on concurrent analgesic use. Therefore, our objective was to evaluate the association between the use of cannabis and codeine. METHODS: We conducted a cross-sectional study using data from the nationally representative Canadian Tobacco, Alcohol and Drugs Survey (2017). The primary explanatory variable was self-reported use of cannabis within the past year. The outcome was the use of codeine-containing product(s) within the past year. We used multivariable binomial logistic regression models. RESULTS: Our study sample comprised 15,459 respondents including 3338 individuals who reported cannabis use within the past year of whom 955 (36.2%) used it for medical purposes. Among individuals who reported cannabis use, the majority were male (N = 1833, 62.2%). Self-reported use of cannabis was associated with codeine use (adjusted odds ratio [aOR] 1.89, 95% CI 1.36 to 2.62). Additionally, when limited to cannabis users only, we found people who used cannabis for medical purposes to be three times more likely to also report codeine use (adjusted odds ratio [aOR] 2.96, 95% CI 1.72 to 5.09). DISCUSSION: The use of cannabis was associated with increased odds of codeine use, especially among individuals who used it for medical purposes. Our findings suggest a potential role for healthcare providers to be aware of or monitor patients' use of cannabis, as the long-term adverse events associated with concurrent cannabis and opioid use remain unknown.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".