Improving the approach to assess impact of anaemia control programs during pregnancy in India: a critical analysis
Bibliographic record
Abstract
Around 42.7% of women experience anaemia during pregnancy in low- and middle-income countries. Countries in southeast Asia (with prevalence ranging between 40 and 60%) have reported a modest decline over the past 25 years. Nearly half the pregnant women continue to be anaemic in India between 2005-06 and 2015-16, although severe anaemia has reduced from 2.2% to 1.3%.India has been committed to achieving a target of 32% prevalence of anaemia in pregnant women from 50% by 2022. There are concerns around stagnancy in the prevalence of anaemia in pregnancy despite a strong political commitment. The paper puts forth the arguments that should be considered while introspecting why India might run the risk of not achieving the expected reduction. The reported findings highlight several methodological issues such as hemoglobin cut-offs used to determine anaemia during pregnancy, method of estimation of Hb, and less emphasis on causes other than iron deficiency anemia.
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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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".