Adherence to prenatal iron-folic acid supplementation in low- and middle-income countries (LMIC): a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Daily iron-folic acid supplementation reduces anemia and various adverse obstetric outcomes such as preterm delivery, low birthweight, hemorrhage, and perinatal and maternal morbidity and mortality. However, its supplementation has not been successful that attributed to several determinants including poor adherence. Therefore, we aimed to conduct a systematic review and meta-analysis on the prevalence and determinants of adherence to prenatal iron-folic acid supplementation in low- and middle-income countries. In addition, we will develop a conceptual framework in the context of low- and middle-income countries (LMIC). We will search PubMed, MEDLINE, EMBASE, EBSCO, Web of Science, SCOPUS, WHO Global Index Medicus, and African Journals Online (AJOL) databases to retrieve relevant literatures. Observational (i.e., case-control, cohort, cross-sectional, survey, and surveillance reports) and quasi-randomized and randomized controlled trial studies conducted in LMIC will be included. The Newcastle-Ottawa Scale (NOS) and Joanna Briggs Institute (JBI) critical appraisal checklist will be used to assess the quality of observational and randomized controlled trial studies respectively. The pooled prevalence and odds ratio of determinants of adherence will be generated using a weighted inverse-variance meta-analysis model. Statistical heterogeneity among studies will be assessed by Cochran’s Q χ2 statistics and Higgins (I2 statistics) method. The result will be presented using forest plots and Harvest plots when necessary. Furthermore, we will perform Jackknife sensitivity and subgroup analysis. Data will be analyzed using comprehensive meta-analysis software (version 2). Contemporary evidence about the prevalence and determinants of adherence in LMIC will be synthesized to generate up-to-date knowledge. To our knowledge, this is the first systematic review. It would have substantial implications for researchers, clinicians, and policymakers for optimizing maternal and child health outcomes in LMIC. The protocol has been registered on International Prospective Register of Systematic Review (PROSPERO), University of York Center for Reviews and Dissemination ( https://www.crd.york.ac.uk/ ), registration number CRD42017080245 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".