Age Demographics Influences Cannabis Use Pre and Post Federal Legalization in Canada and Implications for Clinical Trial Application in Healthy Populations
Notice bibliographique
Résumé
Introduction Cannabis has been used effectively as a medical drug for many years. With the recent federal legalization of cannabis in Canada and hemp in the United States, there is growing consumer interest in how cannabinoids can be efficacious in improving quality of life. The objective of this study was to examine how age influences cannabis use in Southwestern Ontario before and after federal legalization in Canada. Methods A 31 question, online survey was conducted from March 2018 to October 2019 and designed as a market research project to engage with Canadians on past and present medical and recreational cannabis use. The data were used to investigate how age influences self‐reported reasons for use, life stage when started, and method, frequency and component of cannabis most frequently used. Respondents were stratified into age groups (<19 (n=199), 19–24 (n=1182), 25–34 (n=1362), 35–44 (n=1166), 45–54 (n=569), 55–64 (n=537), 65+ (n=82)). Possible differences in cannabis consumption between groups pre and post federal legalization was assessed by the Chi Square test or Fisher’s Exact (2‐tail) test, as appropriate Results There were 2,667 and 2,430 survey respondents’ pre and post‐legalization, respectively. There were significant effects of age group on the cannabinoid consumed, the primary reason for using cannabis, the life stage when started using cannabis, and both the frequency and mode of consumption (p<0.01). There was a demographic shift in those who reported using cannabis for “social/relaxation” purposes following legalization. Prior to legalization, social/relaxation was the most frequently reported reason for use in those <19–24 years. Reducing anxiety was the most frequently reported reason for use for users aged 25–34 and controlling pain was most frequent amongst users aged 35–65+. Following legalization, social/relaxation became the most frequently reported reason for use across a greater age range of <19–45 years. Across all age groups, since legalization, 18–68% reported using cannabis to reduce anxiety, 23–60% to control pain, and 4–27% for digestion/ISB/IBD. These are important findings for consumers of translational research in these areas. Daily users across groups was similar before and after legalization. Prior to legalization, 40–73% reported daily use compared to 48–72% post‐legalization. There were significant differences in use of inhalation (vapor, smoke) and ingestion (oral, sublingual) routes of administration between age groups. Across groups, the most frequently reported route of administration was smoking. There were no differences in suppository (0–9%) and topical (0–18%) use. Conclusion Age influences how and why cannabis is consumed, both before and after federal legalization. Most clinical trials on cannabis have not been completed in healthy populations, which confounds the literature available to inform formulation of investigational products for trails in this population. Moving forward with effective clinical trial design for efficacious use in healthy populations requires an understanding of the needs, perceptions and barriers of consumers, making surveys such as this critical in advancing this choice of treatment modality.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 source (Gemma direct ou Codex distillé), 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 ».