Factors Influencing Anti-Malarial Prophylaxis and Iron Supplementation Non-Compliance among Pregnant Women in Simiyu Region, Tanzania
Bibliographic record
Abstract
Malaria and iron-deficient anemia during pregnancy pose considerable risks for the mother and newborn. Intermittent Preventive Treatment during pregnancy with sulphadoxine-pyrimethamine (IPTp-SP) and iron supplement to prevent anemia to all pregnant women receiving antenatal care (ANC) services is highly recommended. However, their compliance remains low. This study aimed at identifying factors influencing non-compliance of medications among pregnant women. A descriptive cross-sectional study was conducted in Simiyu region in northwest Tanzania using a structured questionnaire to collect data from 430 women who were pregnant or gave birth 12 months prior to data collection. Data were analyzed using non-parametric statistical analysis with STATA 10. Overall, 284 (66%) and 195 (45%) of interviewed women received IPTp-SP and iron supplementation during their ANC visits, respectively. The majority (85%) of women whom received medications were aware if they had received IPTp-SP or iron supplementation. Of those received IPTp-SP, only 11% took all the three doses, while the remaining 89% took only two doses or one dose. For women who received iron supplementation, 29% reported that they did not take any dose at all. Reasons given for not complying with regiments included not liking the medications and disapproval from male partners. Our findings suggest that IPTp-SP and iron supplement compliance among pregnant women in Simiyu region is low. Intensification of community education, further qualitative research and administration of medication through directly-observed therapy (DOT) are recommended to address the problem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".