Cannabis use among Canadian adults with cancer (2007–2016): results from a national survey
Bibliographic record
Abstract
Background: The current study aims to evaluate the rates and associations of cannabis use among Canadian adults with cancer (2007-2016).Methods: Canadian Community Health Survey (CCHS) (2007-2016) was accessed and adult participants who answered yes to the question “Do you have cancer?” and who have complete information about cannabis use were included. Multivariable logistic regression was used to identify factors associated with cannabis use.Results: A total of 4667 participants who currently have cancer were included in the current analysis. The rate of cannabis use increased throughout the study (34.4% in 2015-2016 versus 27.7% in 2007-2008). The following factors were associated with cannabis use: younger age (OR: 3.64; 95% CI: 2.27-5.86; P<0.01); male sex (OR: 2.11; 95% CI: 1.80-2.48; P<0.01); white race (OR: 2.02; 95% CI: 1.46-2.78; P<0.01); single status (OR for married versus single: 0.38; 95% CI: 0.29-0.50; P<0.01) and higher income (OR for income < 20,000 versus income ≥ 80,000: 00.74; 95% CI: 0.56-0.99; P=0.04).Conclusions: Within this study cohort of Canadian adults with current cancer diagnosis, cannabis use is not uncommon. A history of cannabis use is associated with younger age, male sex, white race, non-married status, and higher income.
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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 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| 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".