A mixed methods analysis of cannabis use routines for chronic pain management
Notice bibliographique
Résumé
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, X 2 =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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».