WHO's Global Strategy to Reduce the Harmful use of Alcohol: An Assessment of Recent Policies and Interventions in Finland and Ontario, Canada
Bibliographic record
Abstract
Aim This paper assesses alcohol policies and interventions in Finland and the Canadian province of Ontario, using the policy options and interventions recommended in WHO's Global strategy to reduce the harmful use of alcohol (2010). Data & Methods The information and data are based on archival sources, surveys, legislative and government documents, and published papers. The paper assesses both jurisdictions on 10 areas in the WHO document and their sub-topics: 1. leadership, 2. health services response, 3. community action, 4. drinking and driving policies and countermeasures, 5. availability of alcohol, 6. marketing of alcoholic beverages, 7. pricing policies, 8. reducing the negative consequences of drinking and alcohol intoxication, 9. reducing the public health impact of illicit alcohol and informally produced alcohol, and 10. monitoring and surveillance. Results Ontario had several recent noteworthy developments in line with WHO recommendations: health services response, controls of drinking and driving, pricing policies, reducing the negative consequences of drinking and intoxication, and monitoring and surveillance. Finland has emphasised pricing policies in recent years, and there have also been significant developments in community action, controls of drinking and driving, alcohol advertising, and monitoring and surveillance. Conclusions Challenges and opportunities for strengthening the policy responses are noted, as well as topics for future research.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".