Health Care Provider Preferences for, and Barriers to, Cannabis Use in Cancer Care
Bibliographic record
Abstract
Background: Limited research has been conducted about the perspectives of oncology health care providers (hcps) concerning the use of cannabis in cancer care and their potential role in advising patients. We sought to determine the barriers encountered by hcps with respect to medical cannabis and their preferred practices in this area. Methods: An anonymous survey about cannabis was distributed to oncology hcps at the Tom Baker Cancer Centre in Calgary, Alberta. The 45-question survey measured the opinions of hcps about cannabis use and authorization in oncology. Results: Of 103 oncology hcps who participated in the study, 75% were women. By hcp type, the most commonly reported professional groups were oncology nurse (40%), radiation therapist (9%), and pharmacist (6%). Of respondents, 75% reported providing direct care to cancer patients. More than half (69%) had spoken to a patient about cannabis in the preceding month, and 84% believed that they lacked sufficient knowledge about cannabis to make recommendations. Barriers such as monitoring the patient's use of cannabis (54%), prescribing an accurate dose (61%) or strain (53%), and having insufficient research (50%) were most commonly reported. More than half of hcps (53%) would be interested in receiving more information or training about the use of cannabis in oncology. Conclusions: The survey indicated that this group of oncology hcps believed that they lacked sufficient knowledge about cannabis to make recommendations to patients. In addition to that lack of knowledge, a number of notable barriers were reported, and more than half the hcps indicated interest in learning more about cannabis in the future.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".