Shelter-based managed alcohol administration to chronicallyhomeless people addicted to alcohol
Bibliographic record
Abstract
BACKGROUND: People who are homeless and chronically alcoholic have increased health problems, use of emergency services and police contact, with a low likelihood of rehabilitation. Harm reduction is a policy to decrease the adverse consequences of substance use without requiring abstinence. The shelter-based Managed Alcohol Project (MAP) was created to deliver health care to homeless adults with alcoholism and to minimize harm; its effect upon consumption of alcohol and use of crisis services is described as proof of principle. METHODS: Subjects enrolled in MAP were dispensed alcohol on an hourly basis. Hospital charts were reviewed for all emergency department (ED) visits and admissions during the 3 years before and up to 2 years after program enrollment, and the police database was accessed for all encounters during the same periods. The results of blood tests were analyzed for trends. A questionnaire was administered to MAP participants and staff about alcohol use, health and activities of daily living before and during the program. Direct program costs were also recorded. RESULTS: Seventeen adults with an average age of 51 years and a mean duration of alcoholism of 35 years were enrolled in MAP for an average of 16 months. Their monthly mean group total of ED visits decreased from 13.5 to 8 (p = 0.004); police encounters, from 18.1 to 8.8 (p = 0.018). Changes in blood test findings were nonsignificant. All program participants reported less alcohol consumption during MAP, and subjects and staff alike reported improved hygiene, compliance with medical care and health. INTERPRETATION: A managed alcohol program for homeless people with chronic alcoholism can stabilize alcohol intake and significantly decrease ED visits and police encounters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".