Perceptions of sulphadoxine-pyrimethamine use among pregnant women in sub-Saharan Africa: a scoping review
Bibliographic record
Abstract
Background: Malaria is a major global public health issue that disproportionately affects pregnant women in sub-Saharan Africa. The World Health Organization recommends intermittent preventive treatment with sulfadoxine-pyrimethamine (IPTp-SP) for its control. Despite its proven efficacy, drug uptake remains low. Sulphadoxine-pyrimethamine (SP) safety concerns have been cited as one of several reasons for this low uptake. Methods: We conducted a scoping review using the Arksey and O'Malley framework and the health belief model to investigate perceptions of SP use among pregnant women in sub-Saharan Africa. We looked for peer-reviewed publications in five international databases. Results: The review included 19 articles out of a total of 246. It showed that pregnant women in sub-Saharan Africa have a good understanding of malaria and its consequences, but this does not necessarily translate into increased IPTp-SP uptake. It is worrisome to know that some pregnant women (from 2 studies) did not believe that SP use is beneficial, and several participants (from 4 studies) were unsure or did not see the drug as an effective intervention. Many pregnant women believe SP harms them, their partners, or their unborn children. Conclusions: Healthcare professionals should continue prescribing and encouraging pregnant women to use SP for malaria prevention until a better substitute becomes available.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".