Restricting supply of tobacco products to pharmacies: a scoping review
Bibliographic record
Abstract
OBJECTIVE: We synthesised the published literature on proposals to restrict tobacco supply to pharmacies, covering (1) policy concept/rationale/attempts, (2) policy impact and implementation and (3) policy and research recommendations. DATA SOURCES: We searched eight databases (PubMed, CINAHL, Scopus, Web of Science, Embase, IPA, ProQuest and OATD) for publications with at least an English-language abstract. We searched reference lists of included publications manually. STUDY SELECTION: One author screened all publications, and a second author reviewed a 10% subset. We focused on approaches to restrict the supply of tobacco products to pharmacies, without any restrictions on study design, location, participants or publication date. DATA EXTRACTION: Data extraction adhered to the JBI Scoping Review Methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. DATA SYNTHESIS: We included 18 publications. Among the 13 studies conducted in specific geographical contexts, 8 were from Aotearoa/New Zealand. Most publications (n=8) focused on effectiveness domains, indicating potential reductions in retailer density, smoking prevalence, disease burden, cost and increased opportunities for cessation advice. Seven explored policy acceptability among experts, pharmacists and people who smoke. Publications noted that pharmacy-only supply aligns with other programmes involving pharmacists, such as needle exchange programmes, but conflicts with efforts to phase out tobacco sales from the US and Canadian pharmacies. CONCLUSIONS: Progress in tobacco retailing policy (eg, licensing, retailer incentives) and research (eg, assessment of policy equity and durability, application in other geographical contexts) are needed before a pharmacy-only tobacco supply model would be feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.072 | 0.216 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.038 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".