The Impact Of Alcohol Minimum Unit Pricing On People with experience of Homelessness: Qualitative Study
Bibliographic record
Abstract
ABSTRACT BACKGROUND Alcohol Minimum Unit Pricing (MUP) was introduced in Scotland in May 2018. Existing evidence suggests MUP can reduce alcohol consumption in the general population, but there is little research about its impact on vulnerable groups. This qualitative study aimed to explore experiences of MUP among people with experience of homelessness. METHODS We conducted qualitative semi-structured interviews with a purposive sample of 46 people with current or recent experience of homelessness who were current drinkers when MUP was introduced. Participants (30 men and 16 women) were aged 21 to 73 years. Interviews focused on views and experiences of MUP. Data were analysed using thematic analysis. RESULTS People with experience of homelessness were aware of MUP but it was accorded low priority in their hierarchy of concerns. Reported impacts varied. Some participants reduced their drinking, or moved away from drinking strong white cider in line with policy intentions. Others were unaffected because the cost of their preferred drink (usually wine, vodka or beer) did not change substantially. A minority reported increased involvement in begging. Wider personal, relational and social factors also played an important role in participant responses to MUP. CONCLUSION Our findings suggest that MUP worked as intended for some people with experience of homelessness, while a minority reported negative consequences. Our findings highlight the importance of considering the impact of population level health policies on marginalised groups. Policymakers in Scotland, and elsewhere, need to consider wider contextual factors that affect responses to MUP in people with experience of homelessness. It is important to invest further in secure housing and appropriate support services for people with experience of homelessness who consume alcohol, and implement and evaluate harm reduction initiatives such as managed alcohol programmes.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| 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".