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Record W2338247285 · doi:10.1186/s12954-016-0103-4

Do managed alcohol programs change patterns of alcohol consumption and reduce related harm? A pilot study

2016· article· en· W2338247285 on OpenAlexafffundabout
Kate Vallance, Tim Stockwell, Bernie Pauly, Clifton Chow, Erin Gray, Bonnie Krysowaty, Kathleen Perkin, Jinhui Zhao

Bibliographic record

VenueHarm Reduction Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMacEwan University
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsHealth psychologyMedicineHarm reductionPublic healthAlcohol consumptionAlcohol use disorderAlcoholPsychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Managed alcohol programs (MAPs) are a harm reduction strategy for people with severe alcohol dependence and unstable housing. MAPs provide controlled access to alcohol usually alongside accommodation, meals, and other supports. Patterns of alcohol consumption and related harms among MAP participants and controls from a homeless shelter in Thunder Bay, Ontario, were investigated in 2013. METHODS: Structured interviews were conducted with 18 MAP and 20 control participants assessed as alcohol dependent with most using non-beverage alcohol (NBA). Qualitative interviews were conducted with seven participants and four MAP staff concerning perceptions and experiences of the program. Program alcohol consumption records were obtained for MAP participants, and records of police contacts and use of health services were obtained for participants and controls. Some participants' liver function test (LFT) results were available for before and after MAP entry. RESULTS: Compared with periods off the MAP, MAP participants had 41 % fewer police contacts, 33 % fewer police contacts leading to custody time (x (2) = 43.84, P < 0.001), 87 % fewer detox admissions (t = -1.68, P = 0.06), and 32 % fewer hospital admissions (t = -2.08, P = 0.03). MAP and control participants shared similar characteristics, indicating the groups were broadly comparable. There were reductions in nearly all available LFT scores after MAP entry. Compared with controls, MAP participants had 43 % fewer police contacts, significantly fewer police contacts (-38 %) that resulted in custody time (x (2) = 66.10, P < 0.001), 70 % fewer detox admissions (t = -2.19, P = 0.02), and 47 % fewer emergency room presentations. NBA use was significantly less frequent for MAP participants versus controls (t = -2.34, P < 0.05). Marked but non-significant reductions were observed in the number of participants self-reporting alcohol-related harms in the domains of home life, legal issues, and withdrawal seizures. Qualitative interviews with staff and MAP participants provided additional insight into reductions of non-beverage alcohol use and reductions of police and health-care contacts. It was unclear if overall volume of alcohol consumption was reduced as a result of MAP participation. CONCLUSIONS: The quantitative and qualitative findings of this pilot study suggest that MAP participation was associated with a number of positive outcomes including fewer hospital admissions, detox episodes, and police contacts leading to custody, reduced NBA consumption, and decreases in some alcohol-related harms. These encouraging trends are being investigated in a larger national study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.155
GPT teacher head0.351
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations90
Published2016
Admission routes3
Has abstractyes

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