Enhancing IPTp-SP uptake: Community and stakeholder recommendations for improving access and utilisation – insights from a study in Bayelsa-Nigeria
Bibliographic record
Abstract
Background. Malaria remains a major global health challenge, disproportionately affecting pregnant women and children. In Nigeria, malaria in pregnancy contributes to 70.5% of maternal morbidity and 41.1% of maternal mortality. Recognising these risks, the World Health Organization recommends intermittent preventive treatment with sulfadoxine-pyrimethamine (IPTp-SP) as a key strategy for malaria in pregnancy prevention. However, despite its proven effectiveness, pregnant women’s uptake of IPTp-SP remains unacceptably low. This study presents participant-driven recommendations to enhance IPTp-SP uptake, structured within the socio-ecological framework. Materials and Methods. This study employed an exploratory descriptive qualitative approach to examine the community-level contextual factors influencing IPTp-SP uptake. Data were collected from 53 participants in two communities in Bayelsa, Nigeria. Individual interviews were conducted with 17 key stakeholders (spouses, mothers-in-law, religious leaders, community leaders, and traditional birth attendants) and 6 focus group discussions with 36 pregnant women. Data management and coding were conducted using NVivo 14 QSR International software, following an inductive-deductive thematic analysis approach. Results. Participants proposed multi-level interventions to address barriers to IPTp-SP uptake at the individual, interpersonal, community, and healthcare system levels. Key recommendations include: Community-wide education campaigns to raise awareness of IPTp-SP’s benefits; comprehensive training for healthcare providers to enhance their knowledge and prescription of IPTp-SP; integration of traditional birth attendants into the formal healthcare system; community-level distribution of IPTp-SP to improve access for pregnant women who do not attend antenatal care; government intervention to ensure the functionality of health centers; addressing workforce shortages, and guaranteeing a consistent supply of IPTp-SP. Conclusion. These evidence-based, participant-driven recommendations offer a holistic and scalable strategy to improve pregnant women’s uptake of IPTp-SP in Nigeria and other malaria-endemic regions. Implementing these recommendations can strengthen malaria prevention efforts, improve maternal and child health outcomes, and support broader public health initiatives.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".