Building capacity in reducing population dietary sodium intake in the Americas
Bibliographic record
Abstract
Objective: To present some resources developed as part of the technical support of the Pan American Health Organization (PAHO) to Member States to reduce population dietary sodium intake, and to discuss the main challenges and opportunities to accelerate action toward sodium intake reduction in the Americas. Methods: Sources of information include a mapping of salt reduction policies conducted in 2019, reports from working group meetings, interviews conducted in 2020 and 2021 in seven countries, and technical documents developed around the Updated PAHO Regional Sodium Reduction Targets. Results: These tools show that, despite progress, challenges to succeed in this agenda persist. Priority given to sodium reduction is low in most countries, with insufficient resource allocation. There is a lack of intersectoral coordinated action, and a systemic approach to food systems is commonly missing. Surveillance mechanisms of sodium intake are insufficient, and industry interference in policy processes is commonly identified, undermining policy progress and success. There are also important regional opportunities to address these challenges. These include common ground for future collaborations by updating, strengthening, and complementing these existing tools, and technical and financial support for data generation. Conclusions: PAHO is committed to continue to support countries in the process of promoting, implementing, and monitoring cost-effective sodium reduction interventions. One key policy priority in this agenda is the adoption of the Updated PAHO Regional Sodium Reduction Targets with a mandatory approach, together with the comprehensive and complementary implementation of other strategies. Strong political will and commitment of countries will be critical to translate goals into concrete achievements in the Americas.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".