Factors influencing Canadian oncology nurses discussing cannabis use with patients experiencing chemotherapy-induced nausea
Bibliographic record
Abstract
A descriptive, cross-sectional study was conducted to determine factors influencing Canadian oncology nurses discussing cannabis use with patients experiencing chemotherapy-induced nausea (CIN). A survey invitation and three reminders were sent to 678 members of the Canadian Association of Nurses in Oncology (CANO) between February 8 and April 10, 2022. An educator sent an extra invitation to 131 oncology nurses in Eastern Ontario. The survey was based on the Ottawa Model of Research Use. Twenty-seven opened the link to the survey and 25 responded. Of 25 nurses, 11 (47.8%) correctly answered the knowledge question about the effectiveness of cannabis for CIN. The top three barriers to discussing cannabis use were social stigma, nurses’ lack of knowledge, and lack of guidance within the workplace. All participants identified needing continuing education and written guidance about use of cannabis for CIN. Although few oncology nurses responded to the survey, most indicated feeling inadequately prepared to discuss cannabis use with patients experiencing CIN.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".