Alcohol minimum unit pricing and people experiencing homelessness: A qualitative study of stakeholders' perspectives and experiences
Bibliographic record
Abstract
INTRODUCTION: Minimum unit pricing (MUP) may reduce harmful drinking in the general population, but there is little evidence regarding its impact on marginalised groups. Our study is the first to explore the perceptions of MUP among stakeholders working with people experiencing homelessness following its introduction in Scotland in May 2018. METHODS: Qualitative semi-structured interviews were conducted with 41 professional stakeholders from statutory and third sector organisations across Scotland. We explored their views on MUP and its impact on people experiencing homelessness, service provision and implications for policy. Data were analysed using thematic analysis. RESULTS: Participants suggested that the introduction of MUP in Scotland had negligible if any discernible impact on people experiencing homelessness and services that support them. Most service providers felt insufficiently informed about MUP prior to its implementation. Participants reported that where consequences for these populations were evident, they were primarily anticipated although some groups were negatively affected. People experiencing homelessness have complex needs in addition to alcohol addiction, and changes in the way services work need to be considered in future MUP-related discussions. DISCUSSION AND CONCLUSIONS: This study suggests that despite initial concerns about potential unintended consequences of MUP, many of these did not materialise to the levels anticipated. As a population-level health policy, MUP is likely to have little beneficial impact on people experiencing homelessness without the provision of support to address their alcohol use and complex needs. The additional needs of certain groups (e.g., people with no recourse to public funds) need to be considered.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".