A qualitative exploration of the relevance of training provision in planning for implementation of managed alcohol programs within a third sector setting
Bibliographic record
Abstract
Background: Managed Alcohol Programs (MAPs) are a harm reduction strategy for people experiencing homelessness and alcohol dependence. Despite a growing evidence base, resistance to MAPs is apparent due to limited knowledge of alcohol harm reduction and the cultural preference for abstinence-based approaches. To address this, service managers working in a not-for-profit organization in Scotland designed and delivered a program of alcohol-specific staff training as part of a larger study exploring the potential implementation of MAPs during the COVID-19 pandemic. Methods: Semi-structured interviews were conducted with 15 service managers and staff regarding their experiences of the training provided. Data were analyzed using Framework Analysis, and Lewin's model of organizational change was applied to the findings to gain deeper theoretical insight into data relating to staff knowledge, training, and organizational change. Findings: Participants described increased knowledge about alcohol harm reduction and MAPs, as well as increased opportunities for conversations around cultural change. Findings highlight individual- and organizational-level change is required when implementing novel harm reduction interventions like MAPs. Conclusion: The findings have implications for the future implementation of MAPs in homelessness settings. Training can promote staff buy-in, facilitate the involvement of staff within the planning process, and change organizational culture.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".