Critical assessment of the current indicator for antenatal iron‐containing supplementation coverage: Insights from a mixed‐methods study
Bibliographic record
Abstract
Daily consumption of iron-containing supplements is recommended for all pregnant women but there is no approved global standard indicator for assessing supplementation coverage. Furthermore, the validity of commonly used coverage indicators for iron-containing supplement consumption is questionable. The WHO-UNICEF Technical Expert Advisory Group on Nutrition Monitoring, and partners, have systematically worked to identify a feasible and valid indicator of iron-containing supplement coverage for reporting by countries. In 2019, we conducted key informant interviews with respondents in eight countries, fielded an online survey (in three languages using SurveyMonkey) to which 142 nutrition professionals from 52 countries responded, and used Demographic and Health Surveys (DHS) data from four countries to assess determinants of the quality of iron-containing supplement coverage data. Less than half (45%) of online survey respondents were satisfied with the current methods for collecting iron-containing supplement coverage data in their context. Recommended changes by study respondents include recall period <5 years, adding questions about counselling, including other beneficiary groups, and assessing supply chain functionality. The DHS analysis suggested an association between time since pregnancy and data quality. Data heaping on multiples of 30 was observed in 40%-75% of data. There is a clear demand for a revised indicator and measurement guidance for coverage of iron-containing supplementation during pregnancy. Future research should continue the development and validation of a global indicator, to more precisely validate the quality of recall data, including the distinction between distribution and consumption using various question formulations.
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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 | 0.509 | 0.615 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".