“That’s Pot Culture Right There”: Purchasing Behaviors of People Who Use Cannabis Without a Medical Cannabis Card
Bibliographic record
Abstract
Introduction: The legal landscape surrounding purchasing cannabis without a medical cannabis card (i.e., without MCC) is changing rapidly, affecting consumer access and purchasing behaviors. Cannabis purchasing behaviors are related to subsequent use and experiencing greater cannabis-related negative consequences. However, purchasing behaviors of individuals who use cannabis without MCC are understudied. Methods: The current study analyzed qualitative data from focus groups with adults who use cannabis without MCC (n = 5 groups; 6-7 participants/group; n = 31 total participants). Focus groups followed a semi-structured agenda, and were audio recorded and transcribed. Two coders applied thematic analysis to summarize topics pertaining to cannabis purchasing attitudes and behaviors. Focus groups occurred in 2015 and 2016 in Rhode Island, when purchasing and use of cannabis without MCC was decriminalized but still considered illegal. Results: On average, participants (72% male) were 26 years old (SD = 7.2) and reported using cannabis 5 days per week (SD = 2.1). Thematic analysis revealed three key themes related to cannabis purchasing behaviors: (1) regular purchasing routines (i.e., frequency, schedule, amount of purchases), (2) economic factors (i.e., financial circumstances), and (3) contextual factors (i.e., quality of cannabis, convenience/availability) were perceived to influence purchasing decisions. Dealers' recommendations affected participants' purchases, who also reported minimal legal concerns. Participants reported saving money and using more cannabis when buying in bulk. Discussion: Purchasing behaviors were found to vary and were perceived to be affected by individual-level (e.g., routines) and contextual factors (e.g., availability) that, in turn, may impact use patterns. Future research should consider how factors (e.g., availability) that differ across contexts (e.g., location) and demographic groups interact to affect purchasing behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".