Implementation strategies and outcomes of intravenous iron use for treatment of anaemia during and after pregnancy in LMICs: a scoping review
Bibliographic record
Abstract
Abstract Background There is sufficient evidence of the efficacy of Intravenous (IV) over oral iron in treating anaemia in pregnancy and the postpartum. However, poor implementation can lead to little or no benefit. The objectives of this scoping review are to map and synthesise evidence related to implementation strategies and implementation outcomes of IV iron use for treating anaemia in pregnancy and the postpartum in low- and middle-income countries (LMICs). Methods This scoping review was conducted in accordance with the Joanna Briggs Institute’s methodology for conducting scoping reviews. Electronic databases were searched to identify relevant literature sources published up until June 2025. Two independent reviewers conducted screening using the Covidence software. Descriptive statistics was used to synthesise data and findings were reported narratively. Synthesis of implementation strategies was guided by the Expert Recommendations for Implementing Change compilation. Results Our search yielded 4,589 publications, 20 were included in the review. Ten studies used implementation strategies, mostly “assessment for readiness and identification of barriers and facilitators” (40%; 4/10) and “promotion of adaptability” (30%; 3/10). Fourteen studies mentioned the assessment of implementation outcomes; most assessed were acceptability (42.9%; 6/14) and fidelity (35.7%; 5/14). Only five studies used any theory, model, framework (TMF) or validated measures in the implementation of strategies or assessment of outcomes. Conclusion There is limited implementation research on the use of IV iron for the treatment of anaemia in pregnancy and the postpartum in LMICs. Critically, the use of TMFs and validated measures are deficient in current sources of evidence. More rigorous assessments of the implementation of IV iron for obstetric anaemia in LMICs are required to guide practice, policy and uptake.
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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 | 0.036 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".