Navigating the regulatory landscape: unveiling the factors impacting private cannabis retailers in Canada from a public health perspective
Notice bibliographique
Résumé
Background: The legalization of cannabis in Canada has significantly reshaped the regulatory and retail landscape, presenting both challenges and opportunities for private cannabis retailers. For these retailers, a key challenge lies in balancing retailer profitability with the public health priorities outlined in the Cannabis Act, which emphasizes protecting public health and safety. This tension is particularly pronounced for business owners and operators who must navigate complex regulations while maintaining financial viability. This thesis helps to address this challenge by exploring the factors influencing private cannabis retailers through the Comprehensive Cannabis Retail Framework (CCRF), a novel analytical model I developed that integrates public health principles with retail institutional change theories. Understanding these factors is crucial for fostering a retail environment that supports business viability while advancing public health objectives, such as promoting responsible consumption, minimizing harm, and displacing the unlicensed market. Methods: This dissertation employed a mixed-methods approach across three interconnected studies, each contributing to developing and applying the CCRF. The first study involved a quantitative content analysis of Canadian news media published from January 2017 to March 2022 to identify the barriers faced by private cannabis retailers in Canada. Building on these findings, the second study applied Entman's framing theory to analyze how the media framed these barriers. The third study included qualitative interviews with nine licensed and nine prospective cannabis retailers in Newfoundland and Labrador, exploring the challenges and opportunities they experienced. Insights from each study informed the iterative refinement of the CCRF, which served as both a conceptual foundation and an analytical tool throughout the research. Results: The studies revealed several key factors influencing Canada's cannabis retail market, including government regulations, supply chain issues, economic challenges, socio-cultural factors, and competition from the unlicensed market. Government regulations emerged as the most significant factor. Media coverage frequently attributed these challenges to regulatory burdens and the unlicensed market. Interviews with licensed retailers highlighted challenges like pricing and advertising restrictions, high taxes, and logistical issues, while facilitating factors included product quality and mentorship. Prospective retailers' barriers to entry included high licensing fees, licensing inequity, stigma, and lack of financing. Conclusion and Implications: This research underscores the need for regulatory reform to ensure cannabis retailers sustainability while advancing public health objectives. The CCRF offers a comprehensive framework for understanding the interaction between regulations, retail environments, and public health. These findings provide valuable insights for not only Canada's evolving legal cannabis market but also for other markets around the world.
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 | 0,004 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,020 | 0,012 |
| Communication savante | 0,014 | 0,005 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».