Impact of the COVID-19 pandemic on cannabis cultivation and use in 18 countries
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic and the accompanying measures to mitigate infection affected many areas of society, including the supply and use of cannabis. This paper explored how patterns of behaviour among people who cultivate cannabis were affected by the COVID-19 pandemic and restrictions. METHODS: An anonymous web survey of people who cultivated cannabis was conducted from Aug 2020 to Sep 2021, spanning 18 countries and 11 languages (N = 11,479). Descriptive statistics and mean comparison tests were conducted. RESULTS: Most cannabis growers reported that their practices were relatively unaffected by the COVID-related restrictions. While 35.2 % reported difficulties buying cannabis from their usual dealer, <10 % stated that access to materials needed for growing was impaired during the pandemic. Over one-quarter (28.2 %) of respondents increased their cannabis use and 21.4 % also increased cannabis cultivation (more than twice as many as those who said they were growing less or not anymore) while COVID restrictions were in place. People who lost their job or were casually employed were more likely to increase use and cultivation. Overall, the pandemic had little impact on reasons for growing, however, difficulties obtaining cannabis were mentioned as the most prevalent COVID-19-related growing motive. A small number (16 %) reported starting their growing activity during the pandemic. Italian and Portuguese growers were more likely to report shortages in supply and increases in their growing activity. CONCLUSIONS: This study is the first to document an increase in cannabis cultivation activity following COVID restrictions. Increased home cultivation was not only driven by higher use as a result of home isolation, but also by disruptions of wider illegal cannabis supply. Limitations of this study include the non-representativeness of the sample as well as differences in approaches and duration of restrictions in different countries.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».