An approach to Hemequity: Identifying the barriers and facilitators of iron deficiency reduction strategies in low‐ to middle‐income countries
Bibliographic record
Abstract
Approximately 1.92 billion people worldwide are anaemic, and iron deficiency is the most common cause. Iron deficiency anaemia (IDA) disproportionately affects women of reproductive age and remains under-addressed in low- to middle-income countries (LMICs). The primary objective of our scoping review is to evaluate the barriers and facilitators to IDA management in LMICs by using an intersectionality-enhanced implementation science lens adapted from the consolidated framework for implementation research and the theoretical domains framework. A total of 53 studies were identified. Contextual barriers included the deprioritization of IDA risk, unequal gender norms and stigma from the HIV/AIDS epidemic. Regional poverty, conflict and natural disasters led to supply chain barriers. Individual-level facilitators included partner support and antenatal care access while barriers included forgetfulness and having medical comorbidities. Successful interventions also utilized education initiatives to empower women in community decision-making. Moreover, community mobilization and the degree of community ownership determined the sustainability of IDA reduction strategies. IDA is not only a medical problem, but one that is rooted in the sociocultural and political context. Future approaches must recognize the resilience of LMIC communities and acknowledge the importance of knowledge translation rooted in community ownership and empowerment.
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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 | 0.064 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".