Self-Medication Paths
Bibliographic record
Abstract
OBJECTIVES: Cannabis is used by one-third of people living with chronic pain to alleviate their symptoms despite warnings from several organizations regarding its efficacy and safety. We currently know little about self-medication practices (use of cannabis for therapeutic purposes without guidance), mainly since the legalization of recreational cannabis in countries such as Canada has expanded the scope of this phenomenon. This study aimed to describe legal cannabis self-medication for pain relief in people living with chronic pain and to explore perceptions of the effectiveness and safety of cannabis. METHODS: A cross-sectional descriptive study was performed among 73 individuals living with chronic pain and using cannabis (Quebec, Canada). Data collection using telephone interviews occurred in early 2023. RESULTS: Results indicated that 61.6% of participants reported using cannabis without the guidance of a health care professional (self-medication). Surprisingly, among those, 40.0% held a medical authorization. Overall, 20.6% of study participants were using both medical and legal nonmedical cannabis. Different pathways to self-medication were revealed. Proportion of women versus men participants self-medicating were 58.2% versus 70.6% ( P =0.284). In terms of perceptions, 90.4% of the sample perceived cannabis to be effective for pain management; 72.6% estimated that it posed no or minimal health risk. DISCUSSION: Cannabis research is often organized around medical versus nonmedical cannabis but in the real-world, those 2 vessels are connected. Interested parties, including researchers, health care professionals, and funding agencies, need to consider this. Patients using cannabis feel confident in the safety of cannabis, and many of them self-medicate, which calls for action.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.109 | 0.021 |
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".