Insights from the ground: A qualitative investigation of retailer perspectives of the challenges and opportunities in the legal cannabis market in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
BACKGROUND: The legalization of recreational cannabis in Canada has resulted in varying regulatory and market environments across provinces and territories. These differences shape how retail markets develop and how retailers perceive their opportunities, challenges, and roles in advancing public health objectives. In Newfoundland and Labrador (NL), cannabis retail operates within a distinctive framework shaped by centralized distribution, licensing requirements, and pricing regulations. This qualitative study explores how licensed and prospective retailers perceived the factors influencing the cannabis retail market in NL. METHODS: Semi-structured virtual interviews were conducted with nine licensed and nine prospective cannabis retailers in NL. A thematic analysis, using Wright-Brown et al.'s Comprehensive Cannabis Retail Framework and Ritchie and Spencer's framework analysis, was conducted. Both deductive and inductive coding were applied to identify framework-aligned and emergent themes. RESULTS: Licensed retailers reported challenges such as restrictive advertising rules, high taxation, and supply chain inefficiencies, which they viewed as constraints on profitability and growth. At the same time, access to quality products, positive customer relationships, and informal mentorship networks were seen as enablers of success. Prospective retailers identified high licensing fees, limited access to opportunities, and financing difficulties as significant barriers to entering the legal market. CONCLUSION: This study highlights how NL's cannabis retail system, designed to balance public health protection with market development, may inadvertently limit participation and business sustainability. The study illustrates how regulatory design can shape retailer experiences and market dynamics, underscoring the need to assess whether current regulations are achieving their intended outcomes. While focused on NL, these findings offer valuable insights for other jurisdictions with similar regulatory models, emphasizing the importance of aligning policy design with retailers' experiences to foster a more inclusive, sustainable, and public health-oriented cannabis retail sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".