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Record W3081305295 · doi:10.1111/mcn.12827

Universal Salt Iodisation: Lessons learned from Cambodia for ensuring programme sustainability

2020· article· en· W3081305295 on OpenAlexaff
Karen Codling, Arnaud Laillou, Christiane Rudert, Mam Borath, Jonathan Gorstein

Bibliographic record

VenueMaternal and Child Nutrition · 2020
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsStem Cell Network
FundersUNICEFUnited States Agency for International Development
KeywordsIodised saltIodine deficiencyMedicineGovernment (linguistics)SustainabilityEnforcementPopulationEnvironmental healthEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Iodine deficiency is the leading cause of preventable intellectual disability in the world, but it has been successfully prevented in most countries through universal salt iodization (USI). In 2011, Cambodia appeared to be an example of this success story, but today, Cambodian women and children are once again iodine deficient. In 2011, Cambodia demonstrated high-household coverage of adequately iodized salt and had achieved virtual elimination of iodine deficiency in school-age children. However, this achievement was not sustained because the USI programme was dependent on external funding, and the national government and salt industries had not institutionalized their implementation responsibilities. Recent programmatic efforts, in particular the establishment of a regulatory monitoring and enforcement system, are turning the situation around. Although Cambodia has not yet fully regained the achievements of 2011 (only 55% of tested salt was adequately iodized in 2017 compared with 67% in 2011), the recent steps taken by the government and the salt industry point to greater sustainability of the USI programme and the long-term prevention of iodine deficiency in children, women, and the general population.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.270
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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