Reasonable access: important characteristics and perceived quality of legal and illegal sources of cannabis for medical purposes in Canada
Notice bibliographique
Résumé
BACKGROUND: Throughout the past two decades of legal medical cannabis in Canada, individuals have experienced challenges related to accessing legal sources of cannabis for medical purposes. The objective of our study was to examine the sources of cannabis accessed by individuals authorized to use medical cannabis and to identify possible reasons for their use of illegal sources. METHODS: Individuals who participated in the Cannabis Access Regulations Study (CANARY), a national cross-sectional survey launched in 2014, and indicated they were currently authorized to use cannabis for medical purposes in Canada were included in this study. We assessed differences between participants accessing cannabis from only legal sources versus from illegal sources in relation to sociodemographic characteristics, health-related factors, and characteristics of medical cannabis they considered important. A secondary analysis assessed differences in satisfaction with various dimensions of cannabis products and services provided by legal versus illegal sources. RESULTS: Half of the 237 study participants accessed cannabis from illegal sources. Individuals accessing cannabis from illegal sources were significantly more likely to value pesticide-free products, access to a variety of strains, ability to select strain and dosage, ability to observe and smell cannabis, availability in a dispensary, and availability in small quantities than did individuals accessing cannabis from only legal sources (all p < 0.05). Additionally, participants gave significantly higher satisfaction scores to illegal sources than to legal sources on service-related dimensions of cannabis access (all p < 0.05). CONCLUSION: Our findings contribute to an understanding of reasonable access to medical cannabis from a patient perspective and how to assess whether it has been achieved. Characteristics of cannabis products and services valued by patients and appropriate to their needs should be incorporated into legal medical cannabis programs to promote the use of legal medical sources. While pertaining specifically to medical use of cannabis in Canada, the findings of this study may also be instructive for understanding the use of illegal cannabis sources for non-medical purposes in Canada and provide insight for other jurisdictions implementing cannabis regulations for both medical and non-medical purposes.
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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 | 0,008 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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 ».