Exploring cannabis information and perceptions of cannabis harms among patients with cancer.
Bibliographic record
Abstract
361 Background: Cannabis use is rising among both the general and cancer populations. While the impact of cannabis on cancer care remains unclear, many patients who use cannabis report improvement in their cancer-related symptoms. However, little is known about the potential impact of cannabis on cancer development and treatment outcomes. Understanding patient beliefs about cannabis risks and their awareness of information about cannabis use may be important for future patient education. Here, we explored patient perceptions of harms and information-seeking habits on cannabis. Methods: Patients with cancer were recruited across outpatient ambulatory clinics at the Princess Margaret Cancer Centre (Toronto, Canada) through convenience sampling from July to August 2023. A one-time, paper-based, self-administered questionnaire was used to assess sociodemographic characteristics, perceptions of cannabis harms, and awareness of cannabis information. Multivariable regression analysis was used to identify factors associated with perceptions of cannabis use. Results: Among 243 participants, median age was 65 years, 45% reported lifetime cannabis use (12% were using cannabis at the time of survey), 50% were cigarette smokers, 62% were not employed full-time, and 77% spoke English as a primary language at home. Half (51%) of the participants reported being worried about the potential impacts of cannabis on their health. Current tobacco users (aOR=0.46 [0.19-1.07], p=0.07), primary English speakers at home (aOR=0.53 [0.28-1.01], p=0.06) and those not currently employed (aOR=0.57 [0.33-0.99], p=0.04) were less worried about the effect of cannabis on their health. Despite the perceived harms of cannabis, 84% of participants never sought cannabis-related information, while 40% wanted to know more about the influence of cannabis on cancer risk and treatment. Furthermore, Most (90%) participants reported never having discussed cannabis with their oncologists, but 93% felt it was important for healthcare providers to ask patients about cannabis use. Conclusions: Many patients with cancer are worried about the impact of cannabis on their health and want to know more about cannabis use in cancer care. Most patients report it is important for healthcare providers to have discussions about cannabis use. With the rising prevalence of cannabis use among patients with cancer, validated tools to assess cannabis use and cannabis-related educational resources specific to cancer risk and treatment outcomes should be explored to help address informational needs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".