Drivers of purchase decisions for cannabis products among consumers in a legalized market: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Cannabis was legalized in Canada for non-medical use in 2018. The goal of legalization was to improve health and safety by creating access to regulated products, with accurate product labels and warnings and no risk of contamination. However, more than 2 years post-legalization, a large proportion of purchases are still suspected to be through unlicensed retailers. This study sought to identify the factors that influenced the purchase decisions of cannabis consumers in Newfoundland and Labrador (NL). METHODS: Semi-structured focus groups and interviews were conducted in NL with individuals who were > 19 and had purchased cannabis within the last 12 months. All sessions were conducted virtually, audio-recorded, and transcribed. A thematic analysis was conducted, and two members of the research team coded the data using NVivo. A combination of deductive and inductive coding was carried out, themes from the literature were identified, and new themes from the transcripts were discovered. A final coding template of the data was agreed upon by the team through discussion and consensus. RESULTS: A total of 23 individuals (30% women) participated, with 83% coming from urban areas. While all cannabis product types were discussed, the conversation naturally focused on dried flower products. Participants discussed a variety of considerations when making purchase decisions categorized around five broad themes: 1) price, 2) quality, 3) packaging and warnings, 4) the source of the cannabis, and 5) social influences. The price difference between licensed and un-licensed sources was commonly discussed as a factor that influenced purchase decisions. Product quality characteristics (e.g. size, color, moisture content) and social influences were also considered in purchase decisions. Participants were generally indifferent to packaging and warning labels but expressed concern about the excessive packaging required for regulated products. CONCLUSION: This study explores the many attributes that influence purchase decisions for dried leaf cannabis. Understanding the drivers of purchase decisions can help inform policy reforms to make regulated cannabis products more appealing to consumers. Further research is needed to measure the effect of each attribute on cannabis purchase decisions.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".