The Potential for Managed Alcohol Programmes in Scotland during the COVID-19 Pandemic: A Qualitative Exploration of Key Areas for Implementation Using the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
People experiencing homelessness and alcohol dependence are at increased risk of a range of harms, including from COVID-19. Managed Alcohol Programmes (MAPs) are an alcohol harm reduction intervention specifically for this group. In this paper we report on qualitative findings of a mixed methods study investigating the potential utility of MAPs during the COVID-19 pandemic in Scotland. Interviews, conducted with 40 participants, explored potential views of implementing MAPs during the pandemic. Theoretically, we drew on the Consolidated Framework for Implementation Research (CFIR) to inform data collection and analysis. Six themes were identified which mapped onto three CFIR domains: perceptions of MAPs and the evidence base; necessary components of MAPs; changing culture of alcohol harm reduction; MAPs as a moral and ethical grey area; addressing a service gap; and securing buy-in and partnership working. Participants were generally positive about MAPs and viewed them as a key intervention to address a service gap. Several necessary components were identified for successful implementation of MAPs. Securing buy-in from a range of stakeholders and partnership working were deemed important. Finally, MAPs require careful, long-term planning before implementation. We conclude that MAPs are needed in Scotland and require long-term funding and appropriate resources to ensure they are successful.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".