Integrating and coordinating programs for the management of anemia across the life course
Bibliographic record
Abstract
Anemia is a major global public health concern with a complex etiology. The main determinants are nutritional factors, infection and inflammation, inherited blood disorders, and women's reproductive biology, but the relative role of each varies between settings. Effective anemia programming, therefore, requires evidence-based, data-driven, contextualized multisectoral strategies, with coordinated implementation. Priority population groups are preschool children, adolescent girls, and pregnant and nonpregnant women of reproductive age. Opportunities for comprehensive anemia programming include: (i) bundling interventions through shared delivery platforms, including antenatal care, community-based platforms, schools, and workplaces; (ii) integrating delivery platforms to extend reach; (iii) integrating anemia and malaria programs in endemic areas; and (iv) integrating anemia programming across the life course. Major barriers to effective anemia programming include weak delivery systems, lack of data or poor use of data, lack of financial and human resources, and poor coordination. Systems strengthening and implementation research approaches are needed to address critical gaps, explore promising platforms, and identify solutions to persistent barriers to high intervention coverage. Immediate priorities are to close the gap between access to service delivery platforms and coverage of anemia interventions, reduce subnational coverage disparities, and improve the collection and use of data to inform anemia strategies and programming.
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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 | 0.038 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".