Improving Iron and Folic Acid Supplementation Among Pregnant Women: An Implementation Science Approach in East-Central Uganda
Bibliographic record
Abstract
INTRODUCTION: To address maternal iron-deficiency anemia and low uptake of iron and folic acid supplementation (IFAS) among antenatal care (ANC) clinic attendees in East-Central Uganda, the Anemia Implementation Science Initiative embedded enhanced quality improvement (QI) activities into an integrated health project utilizing QI methodologies. METHODS: To address 2 bottlenecks of stock-outs and inadequate health education for pregnant women during ANC, an enhanced QI intervention was implemented from July 2019 to September 2020 in 2 districts. We conducted a mixed-methods effectiveness quasi-experimental study to assess whether the intervention increased the availability of IFAS in the intervention districts. We used longitudinal facility-level data from 2 treatment districts and 1 comparison district for the quantitative results. Difference-in-difference estimation was used to measure the impact of the intervention on IFAS health education and IFA availability at the health facility. We used logistic regression modeling to control for factors associated with IFAS uptake and potential differences in baseline values. Researchers conducted exit interviews with ANC clients and in-depth interviews with providers and district managers for greater insights into the implementation process. RESULTS: The intervention increased the probability, at a statistically significant level, of pregnant women both receiving IFAS and receiving health education on IFAS during ANC. According to inter-viewees, the intervention approach improved stakeholder engagement and buy-in, which brought about change at all levels of the health system. DISCUSSION: The intervention successfully addressed the 2 main bottlenecks to availability of IFAS for pregnant women attending ANC-inadequate provision of IFAS education and a weak drug quantification process. Even without additional funds to purchase commodities, this approach improved district capacity to advocate for and manage IFAS commodities. It could also be used to strengthen overall ANC quality.
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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 | 0.039 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 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".