Healthcare practitioner perceptions on barriers impacting cannabis prescribing practices
Bibliographic record
Abstract
Abstract Background Canadians seeking medical cannabis (MC) may encounter difficulties in finding a healthcare provider (HCP) who authorizes their access to it. Barriers that HCPs face in authorizing MC are unclear. The objectives of this study were to evaluate HCP opinions, knowledge, comfort, and practice in MC prescribing and counseling on recreational cannabis use, and whether the COVID-19 pandemic affected MC prescribing practices. Methods Eligible participants included HCPs (e.g., attending physicians, nurses, pharmacists) in Canada. A questionnaire evaluating their knowledge, comfort, and practice in medical and recreational cannabis was designed based on instruments developed in previous studies. Between April 13th-December 13th 2021, ninety-one healthcare associations were asked to distribute the survey to their members, and an advertisement was placed in the online Canadian Medical Association Journal. Descriptive statistics were used to analyze the results. Results Twenty-four organizations agreed to disseminate the survey and 70 individuals completed it. Of respondents, 71% were attending physicians or medical residents, while the remainder were nurses, pharmacists or other HCPs. Almost none (6%) received training in MC in professional school but 60% did receive other training (e.g., workshops, conferences). Over half (57%) received more questions regarding MC since recreational cannabis was legalized, and 82% reported having patients who use MC. However, 56% felt uncomfortable or ambivalent regarding their knowledge of MC, and 27% were unfamiliar with the requirements for obtaining MC in Canada. The most common symptoms for recommending MC were pain and nausea, whereas the most common conditions for recommending it were cancer and intractable pain. The strongest barrier to authorizing MC was uncertainty in safe and effective dosage and routes of administration. The strongest barrier to recommending or authorizing MC was the lack of research evidence demonstrating its safety and efficacy. During the pandemic, many respondents reported that a greater number of their patients used cannabis to relieve anxiety and depression. Conclusions Our results suggest that HCPs across Canada who responded to our survey are unfamiliar with topics related to MC. The strongest barriers appear to be lack of clinical research, and uncertainty in safe and effective MC administration. Increasing research, training, and knowledge may help HCPs feel more equipped to make informed treatment/prescribing decisions, which may help to improve access to MC.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".