Prevalence and determinants of anemia among women of reproductive age in developing countries of Asia and Africa: A systematic review protocol
Bibliographic record
Abstract
Abstract Background The burden of anemia among women of reproductive age (WRA) is significantly higher in developing countries. Studies have been conducted to estimate the prevalence and magnitude of anemia among women of reproductive age and its associated factors mainly in African and Asian countries. However, findings from the existing literature are not synthesized in a systematic way to make evidence-based decisions. Given the high burden of anemia in Asian and African countries and an enormous amount of literature on the prevalence and determinants of anemia, there is a need to undertake a systematic review on prevalence and determinants of anemia among WRA in developing countries of Asia and Africa. Hence, the objective of this study is to conduct a systematic review on the prevalence and determinants of anemia among WRA in developing countries of Asia and Africa. This synthesis of literature will help to understand the determinants of anemia to design targeted and context-based interventions primarily for WRA. In addition, this systematic review will also assist policymakers, stakeholders and managers to make informed and evidence-based decisions mainly in developing countries of Asia and Africa.Methods The review will focus on assessing the burden of anemia and its determinants in the developing countries of Asia and Africa. PubMed, CINAHL Plus, Science Direct, Medline and Cochrane will be explored using a detailed search strategy. A study will be considered eligible, if it will be a primary epidemiological research conducted through quantitative observational study designs (cross-sectional, case-control or cohort studies) on prevalence and determinants or only determinants of anemia among WRA (15-49 years) in the developing country of Africa or Asia and published in the English language in a peer-reviewed local or international journal from 2000 to 2020.Discussion This systematic review will provide an opportunity to critically appraise and synthesize the findings from individual studies on the prevalence and determinants of anemia to finally generate a summary. These aggregated findings, in turn, can guide practice and inform policymakers to make evidence-based decisions to address the problem of anemia among women of reproductive age. The results of the review will be disseminated through presentations and peer-reviewed publications.
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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 | 0.034 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.004 |
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".