Enlighten architecture students on environmental design through computer simulation
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Managed alcohol programs (MAP) are a harm reduction approach for those experiencing alcohol use disorders (AUD) and homelessness. These programs were developed in Canada and have had positive results; very few exist in the UK and Ireland. The aim of this study was to scope the feasibility and acceptability of implementing MAPs in Scotland. DESIGN AND METHODS: Using mixed-methods, we conducted two linked phases of work. Quantitative data were collected from the case records of 33 people accessing eight third sector services in Scotland and analysed in SPSS using descriptive and inferential statistics. 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). Data were analysed using Framework Analysis in NVivo. RESULTS: The case record review revealed high levels of alcohol use, related health and social harms, illicit drug use, withdrawal symptoms, and mental and physical health problems. Most participants highlighted a lack of alcohol harm reduction services and the potential of MAPs to address this gap for this group. DISCUSSION AND CONCLUSIONS: Our findings highlight the potential for MAPs in Scotland to prevent harms for those experiencing homelessness and AUDs, due to high levels of need. Future research should examine the implementation of MAPs in Scotland in a range of service contexts to understand their effectiveness in addressing harms and promoting wellbeing for those experiencing AUDs and homelessness.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.011 |
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".