CISUR Bulletin 20: Scale up of Managed Alcohol Programs
Bibliographic record
Abstract
In the landscape of illicit drug harm reduction and alcohol policy, there are few options for those impacted by the harms of high risk or illicit drinking (unsafe settings, unsafe sources such as nonbeverage alcohol and unsafe patterns of consumption) and/or severe alcohol dependence. \nThese harms are not new but are being escalated during the response to COVID-19, creating a surge of unmet need and propelling interests in the development of Managed Alcohol Programs (MAPs) across Canada and elsewhere. \nIn this bulletin, we provide some beginning guidance and suggestions for organizations looking to initiate or scale up a MAP. There is substantial and growing evidence that MAPs are a unique intervention to reduce harms related to high risk drinking, severe alcohol dependence, homelessness and poverty. \nPlease see www.cmaps.ca for more detailed information on the Canadian Managed Alcohol Program Study (CMAPS). This guidance is based on six common elements of MAPs, CMAPS research on implementation and outcomes as well as extensive experience and wisdom of the CMAPS community of practice. This bulletin focuses on frequently asked questions received by the CMAPS team.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".