The Compassion Club: A New Proposal for Transformation of Tobacco Retail
Bibliographic record
Abstract
INTRODUCTION: One major assumption in the current tobacco industry is the distribution of tobacco products through a system of commercial for-profit retail. However, other models of distribution that do not rely on this mechanism exist. AIMS AND METHODS: In this review, we examine the potential of a nonprofit Compassion Club model and discuss how the current existence of independent vape stores might provide the infrastructure to allow the transformation of tobacco distribution. RESULTS: Compassion Clubs exist internationally with different levels of regulation and legality and have generally been focused on the distribution of illegal drugs or hard-to-access pharmaceuticals. They provide access to drugs for existing users, limit access by novices, limit negative impacts from illicit markets, and provide social support focused on reducing harms associated with drug use. CONCLUSIONS: With decreasing prevalence of tobacco use in many countries and growing interest in a tobacco endgame, a Compassion Club model of distribution could help transition tobacco away from the model of commercial widely available distribution. More work is needed to develop the regulations and policies that might guide a compassion club model. IMPLICATIONS: Compassion clubs are a model for the distribution of psychoactive substances that are focused on harm reduction and social support rather than profit. There has been little discussion about the possibility that this promising model could be applied to help transform the tobacco industry. Many independent vape stores already demonstrate aspects of the compassion club model that could be used to support a transition.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".