Exploring the Potential of Implementing Managed Alcohol Programmes to Reduce Risk of COVID-19 Infection and Transmission, and Wider Harms, for People Experiencing Alcohol Dependency and Homelessness in Scotland
Bibliographic record
Abstract
People who experience homelessness and alcohol dependency are more vulnerable than the general population to risks/harms relating to COVID-19. This mixed methods study explored stakeholder perspectives concerning the impact of COVID-19 and the potential utility of introducing managed alcohol programmes (MAPs) in Scotland as part of a wider health/social care response for this group. Data sources included: 12 case record reviews; 40 semi-structured qualitative interviews; and meeting notes from a practitioner-researcher group exploring implementation of MAPs within a third sector/not-for-profit organisation. A series of paintings were curated as a novel part of the research process to support knowledge translation. The case note review highlighted the complexity of health problems experienced, in addition to alcohol dependency, including polysubstance use, challenges related to alcohol access/use during lockdown, and complying with stay-at-home rules. Qualitative analysis generated five subthemes under the theme of 'MAPs as a response to COVID-19': changes to alcohol supply/use including polysubstance use; COVID-19-related changes to substance use/homelessness services; negative changes to services for people with alcohol problems; the potential for MAPs in the context of COVID-19; and fears and concerns about providing MAPs as a COVID-19 response. We conclude that MAPs have the potential to reduce a range of harms for this group, including COVID-19-related harms.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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 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".