Estimation of Unrecorded Alcohol Consumption in Low‐, Middle‐, and High‐Income Economies for 2010
Bibliographic record
Abstract
BACKGROUND: Consumption of unrecorded alcohol is prevalent, especially in low-income countries (LIC). Monitoring and reduction of unrecorded consumption have been asked for in the World Health Organization (WHO) global strategy to reduce the harmful use of alcohol. To date, only a few countries have installed monitoring systems, however. METHODS: As part of the WHO global monitoring, an expert survey using the nominal group technique, a variant of Delphi studies, was conducted to assess level and characteristics of unrecorded consumption in 46 member states. One hundred experts responded. Descriptive statistics and repeated analysis of covariance were used to analyze the data. RESULTS: The study showed feasibility of the chosen methodology to elicit information of unrecorded consumption with experts responding for 74% of the countries. Response rate was lower for LIC. Compared to prior WHO estimates, experts tended to estimate higher unrecorded consumption for LIC, and lower unrecorded consumption for high-income countries. Unrecorded consumption was seen as a financial, public health, and social problem by the majority of experts. Homemade alcohol was the most prevalent subcategory of unrecorded consumption globally. CONCLUSIONS: The chosen methodology was feasible, and new information about consumption of unrecorded consumption could be gathered. There is still a need for increasing efforts of national monitoring, especially in LIC.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".