Motivations and expectations for using cannabis products to treat pain in humans and dogs: a mixed methods study
Notice bibliographique
Résumé
Abstract Background Social media and academic literature suggest that more people are using cannabis to treat their own or their dog’s chronic pain. This study identifies the reasons people use cannabis products to treat their own pain or their dog’s pain and explores whether these products have fulfilled their expectations. Methods An anonymous, online survey was used to collect quantitative and qualitative self-report data on respondents’ perceptions, motivations and expectations about their or their dog’s chronic pain and cannabis use. The analyses are based on U.S. adults who reported using cannabis products to treat their own ( N = 313) or their dog’s ( N = 204) chronic pain. Quantitative responses from the two groups were compared using Chi-Square tests and qualitative data were analyzed using a thematic analysis. Results Human patients and dog owners reported similar motivations for using cannabis products to treat chronic pain, with the more popular reasons being that cannabis products are natural, are preferred over conventional medication, are believed to be the best treatment or good treatment option for pain. Similar proportions of human patients and dog owners reported that the use of cannabis products fulfilled their expectations (86% vs. 82% respectively, χ 2 (1, 200) = .59, p = .32). The qualitative data revealed that their expectations were met by reducing pain, increasing relaxation, and improving sleep, coping, functionality and overall well being. Additionally, the qualitative data suggests that cannabis products offer a return to normalcy and a restored sense of self to human and dog patients. Conclusions The results suggest that people choose cannabis products because they are natural and a possible solution to managing chronic pain when conventional medicines have not been effective. Most people report that their expectations regarding pain management are fulfilled by these products. More accurate assessments are vital, however, for understanding both the objective biomedical and subjective socioemotional benefits of cannabis products for effective pain management for human and dog patients. In addition, objective factual information regarding cannabis products for effective pain management in humans and dogs is needed. It is recommended that both physicians and veterinarians work towards feeling more comfortable proactively broaching the subject of cannabis use with additional training and education.
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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 | 0,007 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».