Assessing the Policy Landscape for Salt Reduction in South-East Asian and Latin American Countries – An Initiative Towards Developing an Easily Accessible, Integrated, Searchable Online Repository
Bibliographic record
Abstract
Background: High dietary salt intake is an avoidable cause of hypertension and associated cardiovascular diseases (CVDs). Thus, salt reduction is recommended as one of the most cost-effective interventions for CVD prevention and for achieving the World Health Organization's (WHO) 25% reduction in premature non-communicable disease (NCD) mortality by 2025. However, current and comprehensive information about national salt reduction policies and related actions across different regions are difficult to access and impede progress and monitoring. Objectives: As an initial step to developing an online repository of salt reduction policies and related actions, and to track nation-wise progress towards the WHO's 25 by 25 goal, we aimed to identify and assess salt reduction policies and actions in select countries from two of the top five most populous regions of the world- the South-East Asia and Latin America. Methods: We conducted a literature review to identify national and regional salt reduction policies in the selected South-East Asian and Latin American countries, from January 1990-August 2020, available in English and Spanish. We also contacted selected WHO country offices (South-East Asian region) or relevant national authorities (Latin America) to gain access to unpublished documents. Results: In both regions, we found only a few dedicated stand-alone salt reduction policies: Bhutan, Sri-Lanka and Thailand from South East Asia and Costa Rica from Latin America. Available polices were either embedded in other national health/nutritional policy documents/overall NCD policies or were unpublished and had to be accessed via personal communication. Conclusions: Salt reduction policies are limited and often embedded with other policies which may impede their implementation and utility for tracking national and international progress towards the global salt reduction target associated with the 25 by 25 goal. Developing an online repository could help countries address this gap and assist researchers/policymakers to monitor national progress towards achieving the salt reduction target.
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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 | 0.028 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.029 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".