Perceptions and engagement of patients with chronic conditions on the use of medical cannabis: a scoping review
Bibliographic record
Abstract
Abstract Context Studies generally focus on one type of chronic condition and the effect of medical cannabis (MC) on symptoms; little is known about the perceptions and engagement of patients living with chronic conditions regarding the use of MC. Objectives This scoping review aims to explore: (1) what are the dimensions addressed in studies on MC that deal with patients' perceptions of MC? and (2) how have patients been engaged in developing these studies and their methodologies? Through these objectives, we have identified areas for improving future research. Methods We searched five databases and applied exclusion criteria to select relevant articles. A thematic analysis approach was used to identify the main themes: (1) reasons to use, to stop using or not to use MC, (2) effects of MC on patients themselves and empowerment, (3) perspective and knowledge about MC, and (4) discussion with relatives and healthcare professionals. Results Of 53 articles, the main interest when assessing the perceptions of MC is to identify the reasons to use MC (n = 39), while few articles focused on the reasons leading to stop using MC (n = 13). The majority (85%) appraise the effects of MC as perceived by patients. Less than one third assessed patients’ sense of empowerment. Articles determining the beliefs surrounding and knowledge of MC (n = 41) generally addressed the concerns about or the comfort level with respect to using MC. Only six articles assessed patients’ stereotypes regarding cannabis. Concerns about stigma constituted the main topic while assessing relationships with relatives. Some articles included patients in the research, but none of them had co-created the data collection tool with patients. Conclusions Our review outlined that few studies considered chronic diseases as a whole and that few patients are involved in the co-construction of data collection tools as well. There is an evidence gap concerning the results in terms of methodological quality when engaging patients in their design. Future research should evaluate why cannabis’ effectiveness varies between patients, and how access affects the decision to use or not to use MC, particularly regarding the relationship between patients and healthcare providers. Future research should consider age and gender while assessing perceptions and should take into consideration the legislation status of cannabis as these factors could in fact shape perception. To reduce stigma and stereotypes about MC users, better quality and accessible information on MC should be disseminated.
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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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".