Training healthcare workers increases IFA use and adherence: Evidence and cost‐effectiveness analysis from Bangladesh
Bibliographic record
Abstract
Iron and folic acid (IFA) supplementation programmes are important for preventing and controlling anaemia among pregnant women in low- and middle-income countries. However, frontline health care workers often have limited capacity and knowledge, which can compromise such programmes' effectiveness. Between 2012 and 2014, Nutrition International and the Government of Bangladesh implemented a programme intended to increase IFA supplement consumption during pregnancy. The programme provided frontline health care workers with training on the benefits of IFA supplementation, the use of interpersonal communication and health promotion materials during antenatal care visits and health management information systems to track reported adherence to IFA supplementation. Using a quasi-experimental design, this study investigates the programme's effectiveness and cost-effectiveness at increasing IFA supplement consumption and adherence among pregnant women. The difference-in-differences regression analysis comparing outcomes in an intervention and comparison group concluded that the programme increased IFA consumption by an average of 45.05 supplements (P value = 0.018) and increased the share of women that reported adherence to a regime of at least 90 supplements by 40.35 percentage points (P value = 0.020). Knowledge of IFA supplement dosage and benefits also increased among frontline health care workers and pregnant women. The programme cost $47.11 USD (2018) per disability-adjusted life year averted, which is considered highly cost-effective when evaluated against several cost-effectiveness thresholds. This study suggests that the capacity building of frontline health care workers is an effective and cost-effective method of preventing and controlling anaemia among pregnant women in resource-constrained areas.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".