A mixed methods analysis of cannabis use routines for chronic pain management
Bibliographic record
Abstract
Abstract Background The wide heterogeneity of available cannabis products makes it difficult for physicians to appropriately guide patients. In the current study, our objective was to characterize naturalistic cannabis use routines and explore associations between routines and reported benefits from consuming cannabis. Methods We performed a mixed methods analysis of n=1087 cross-sectional survey responses from adults with self-reported chronic pain using cannabis for symptom management in the USA and Canada. First, we qualitatively analyzed responses to an open-ended question that assessed typical cannabis use routines, including administration routes, cannabinoid content, and timing. We then sub-grouped responses into categories based on inhalation (smoking, vaporizing) vs. non-inhalation (e.g., edibles). Finally, we investigated subgroups perceptions of how cannabis affected pain, overall health, and use of medications (e.g., substituting for opioids, benzodiazepines). Substitutions were treated as a count of medication classes, while responses for both pain and health were analyzed continuously, with − 2 indicating health declining a lot or pain increasing a lot and 2 indicating that health improved a lot or pain decreased a lot. Results Routines varied widely in terms of administration routes, cannabinoid content, and use timing. Overall, 18.8%, 36.2%, and 45% used non-inhalation, inhalation, and non-inhalation + inhalation routes, respectively. Those who used inhalation routes were younger (mean age 46.5 [inhalation] and 49.2 [non-inhalation + inhalation] vs. 56.3 [inhalation], F=36.1, p<0.001), while a higher proportion of those who used non-inhalation routes were female (72.5% non-inhalation vs. 48.3% inhalation and 65.3% non-inhalation + inhalation, X2=59.6, p<0.001). THC-rich products were typically used at night, while CBD-rich products were more often used during the day. While all participants reported similarly decreased pain, participants using non-inhalation + inhalation administration routes reported larger improvements in health than the non-inhalation (mean difference = 0.32, 95% CI: 0.07–0.37, p<0.001) and inhalation subgroups (mean difference = 0.22, 95% CI: 0.07–0.37, p=0.001). Similarly, the non-inhalation + inhalation group had significantly more medication substitutions than those using non-inhalation (mean difference = 0.62, 95% CI: 0.33–0.90, p<0.001) and inhalation administration routes (mean difference = 0.45, 95% CI: 0.22–0.69, p<0.001), respectively. Conclusions Subgrouping medical cannabis patients based on administration route profile may provide useful categories for future studies examining the risks and benefits of medical cannabis.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".