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. These 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. In 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. Please 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 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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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".