Enablers and Barriers of Zinc Fortification; Experience from 10 Low- and Middle-Income Countries with Mandatory Large-Scale Food Fortification
Bibliographic record
Abstract
Adequate zinc nutrition is important for child growth, neurodevelopment, immune function, and normal pregnancy outcomes. Seventeen percent of the global population is estimated to be at risk for inadequate zinc intake. However, zinc is not included in the fortification standards of several low- and middle-income countries with mandatory fortification programs, despite data suggesting a zinc deficiency public health problem. To guide policy decisions, we investigated the factors enabling and impeding the inclusion of zinc as a fortificant by conducting in-depth interviews with 17 key informants from 10 countries. Findings revealed the decision to include zinc was influenced by guidance from international development partners and enabled by the assessment of zinc deficiency, mandatory regional food fortification standards which included zinc, the World Health Organization (WHO) guidelines for zinc fortification, and the low cost of zinc compound commonly used. Barriers included the absence of zinc from regional fortification standards, limited available data on the efficacy and effectiveness of zinc fortification, and the absence of national objectives related to the prevention of zinc deficiency. To promote zinc fortification there is a need to put the prevention of zinc deficiency higher on the international nutrition agenda and to promote large-scale food fortification as a key deficiency mitigation strategy.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".