The evolution, progress, and future direction of Nepal's universal salt iodization program
Bibliographic record
Abstract
Nepal is located in what was once known as the Himalayan Goitre Belt and once had one of the highest prevalence's of iodine deficiency disorders in the world. However, through a well-executed universal salt iodization program implemented over the past 25 years, it has achieved optimal iodine intake for its population, effectively eliminating the adverse consequences of iodine deficiency disorders. A comprehensive review of policy and legislation, surveys, and program reports was undertaken to examine the key elements contributing to the success of this program. The paper reviews the origins and maturation of salt iodization in Nepal, as well as trends in the coverage of iodized salt, the iodine content in salt, and population iodine status over the past two decades. The paper describes critical components of the program including advocacy efforts, trade issues with India, the role of the Salt Trading Corporation, monitoring, and periodic program reviews. The paper discusses the recent findings from the 2016 national micronutrient survey demonstrating the success of the salt iodization program and describes emerging challenges facing the program in the future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".