Co-use of cannabis with commonly used licit and illicit drugs
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Notice bibliographique
Résumé
Background: Following the recent legalization of medical cannabis in Canada, and many other countries around the world, people are turning to this drug for both medical and recreational reasons. Naturally, as human’s age, many rely on medication to maintain a better quality of life. Surveys show that, once legal, there will be an increase in cannabis consumption. Many adverse health reactions may occur by concurrently taking cannabis and other medications. Methods: A survey was distributed in-person throughout Vancouver targeting people who do not consume cannabis. The same survey was distributed in Vancouver, but to people coming out of dispensaries, targeting people who do consume cannabis. The survey consisted of seven knowledge questions asking about possible adverse drug interactions occurring between cannabis and commonly used licit and illicit drugs. A chi-square analysis was used to compare knowledge of users and non-users of cannabis. Results: Both users and non-users seemed to be most knowledgeable on the interaction between cannabis and alcohol; 39 out of 57 users (68%) and 23 out of 30 non-users (77%) gave the correct response. As for all the other interactions, neither group was very knowledgeable. The distribution of questions that were answered incorrectly seemed evenly spread between the two groups. The knowledge between users and non-users were significantly different when participants were asked on the possible adverse reactions between cannabis and opioid drugs (p=0.005), and cannabis and sedative drugs (p=0.002). In these cases, cannabis users were more knowledgeable about cannabis interactions than non-users. Conclusion: This study indicates that the general public is not very knowledgeable on the possible adverse reactions that may come about as a result of mixing cannabis and other commonly used licit and illicit drugs. Actions should be taken to provide the public with tools that will aid them in making the right decision when thinking about concurrently using cannabis and other licit and/or illicit drugs.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,001 | 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 découle