A narrative review of alcohol control policies in Latvia between 1990 and 2020
Bibliographic record
Abstract
ISSUES: Latvia has one of the highest alcohol per capita consumption in Europe. This study provides a narrative review of all evidence-based population-level alcohol control policies implemented in Latvia during the past 30 years. APPROACH: A review of country-level alcohol control policies implemented in Latvia between 1990 and 2020 was conducted. The World Health Organization's "best buys" and other recommended interventions for alcohol control were used to guide the search. KEY FINDINGS: Alcohol control policies in Latvia have evolved significantly over the last three decades. The most changes to alcohol control policy occurred in the transitional period between regaining independence in 1991 and joining the European Union in 2004. A number of significant alcohol control policies have been implemented to reduce alcohol availability and affordability, to restrict alcohol marketing and to counter drunk-driving. However, since 2010, when an increasing trend of alcohol consumption was observed, there has been a reluctance to pursue national public health policy actions to reduce alcohol consumption, and few adjustments to legislation to increase alcohol control have been made. IMPLICATIONS: Despite the progress in alcohol control, Latvia still has considerable potential for strengthening alcohol control to reduce the high levels of alcohol consumption. CONCLUSION: Although several alcohol control policies have been established in Latvia, many of the planned activities to limit alcohol intake and related harm have not been executed. Public health goals rather than political and economic incentives should be prioritised to reduce high levels of alcohol consumption in Latvia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".