Access to healthcare among managed alcohol program participants: A mixed methods study
Bibliographic record
Abstract
Managed alcohol programs (MAPs) offer safe and regulated doses of alcohol to individuals with high-risk drinking behaviours unresponsive to other treatments. These harm reduction programs aim to reduce alcohol-related harms and increase access to housing, health, and social services. In our study we aimed to understand the impacts of MAP participation on access to healthcare. Using a mixed methods design, we analyzed data collected in six Canadian cities between 2014 and 2017. Data sources included surveys from MAP participants (n = 188) and locally recruited and matched control participants (n = 198), and semi-structured interviews with MAP participants (n = 56). In the quantitative cross-sectional analysis, MAP participants were more likely to report regular (OR 1.77 [1.02 - 3.07]) and satisfactory (OR 2.02 [1.04 - 3.91]) access to healthcare compared to controls. We identified variable findings in access to healthcare between a subset of MAP (n = 82) and control (n = 116) participants with 12-month longitudinal follow-up data; however, when MAP participants were on the program, they had an increased likelihood of reporting regular (OR 2.16 [1.04 - 4.48]) and satisfactory (OR 3.23 [1.09 - 9.54]) access to healthcare compared to when they were off the program. Themes generated from the qualitative analysis illustrated the services offered within and alongside MAPs, the impacts of the MAP environment on access to healthcare, and the time needed to develop trusting relationships and address complex needs. These findings highlight the importance of long-term supportive care to improve access to healthcare among those experiencing homelessness and alcohol use disorders.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".