<i>‘It’s like a safety haven’</i>: considerations for the implementation of managed alcohol programs in Scotland
Bibliographic record
Abstract
Alcohol use disorders (AUDs) are not equitably spread across the population, with some groups, such as people who are experiencing homelessness, being more vulnerable to AUDs due to social inequalities, stigma, and complex social and structural processes. Managed alcohol programs (MAPs) are a harm reduction approach first developed in Canada for those experiencing AUDs and homelessness with positive results. This study aimed to describe the factors that should be considered when implementing MAPs in Scotland. Qualitative data were collected in Scotland via semi‐structured interviews with 29 individuals in a range of roles, including strategic informants (n = 12), service staff (n = 8), and potential beneficiaries (n = 9). Vignettes were used to support data collection. Data were analysed using Framework in NVivo. Participants highlighted six considerations to inform the implementation of MAPs in Scotland: the importance of individualized care; provision of alcohol; holistic care and a focus on well-being; types of settings and service models; staffing; and autonomy and rules. Future research should focus on piloting MAPs in a range of service contexts, using different models of care and settings, to develop an enhanced understanding of their effectiveness in addressing harms and promoting well-being for those experiencing AUDs and homelessness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".