Introducing double fortified salt in social safety net programmes in Madhya Pradesh and Gujarat in India: Success factors, challenges and lessons learned
Bibliographic record
Abstract
Double fortified salt (DFS; with iron and iodine) was introduced in social safety net programmes (SSNPs) in Madhya Pradesh (MP) and Gujarat states in 2018. Nutrition International (NI) provided critical support for the intervention. An impact evaluation in MP found high DFS uptake, exceeding 90%. Conduct a process evaluation of the DFS programmes in MP and Gujarat states to identify success factors, challenges, and recommend considerations for scale-up. Twenty-eight qualitative interviews were conducted with NI staff, national and state level government officials, and DFS producers in 2022. Enabling environmental factors included national-level support for food fortification, consensus that anaemia was essential to address, and institutional trust in NI for technical assistance. In programme implementation, the primary challenges were reports of black specks in DFS and the darkening of food cooked with DFS. NI supported the government in improving handling practices, ensuring a regular and stable supply, introducing quality monitoring efforts and launching targeted behaviour change communication (BCC) campaigns regarding the value of DFS. Long-term implementation of the programmes is a weak point, as DFS production is more expensive than iodised salt, there is no existing market outside of institutional demand, and BCC must be long-term, high-quality, and requires resourcing for continued high uptake among SSNP beneficiaries. Strong government buy-in and technical support along the supply chain to address quality issues and beneficiary acceptance were key factors for the successful introduction of DFS. Comparative studies of DFS programmes should be conducted to improve confidence in the success factors that lead to high DFS uptake.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".