Prevalence of Anemia and Its Risk Factors among Pregnant Women in Dakar and Fatick Regions, Senegal
Bibliographic record
Abstract
Objectives: Iron Folic acid supplementation (IFAS) during pregnancy has been implemented in Senegal for many years. However, prevalence of anemia is still high among pregnant women. To provide data for program improvement, we conducted a study to assess the prevalence of anemia and associated risk factors in Dakar and Fatick regions. Methods: The study was a cross-sectional, descriptive survey of 483 randomly selected pregnant women in Dakar and Fatick regions. Hemoglobin level was determined by HemoCue photometer, and questionnaires were used to collect data on socio-economic characteristics, knowledge, attitudes, and practices. Data were cleaned, coded and analyzed with Epi Info. Financial barriers were defined as: family decision makers who restrict expenditures, limited financial resources. Results: Results showed that the prevalence of anemia (Hb <11g/dL) among pregnant women was 66.4% and 71.4% in Dakar and Fatick regions, respectively. Pregnant women had lower risks of being anemic when they had an income-generating activity (OR=1.63, CI (1.1 -2.5)), had been supplemented for at least 90 days (OR=2.26, CI (1.2 -4.3)), and had no financial barrier to access IFAS (OR=0.40, CI (0.1 -0.9]. 50% of the pregnant women wrongly associated side effects to IFA consumption and consequently initiated IFAS after the 2 nd trimester. Conclusions: Anemia is a severe public health problem among pregnant women in these two regions. Interventions designed to address anemia should improve accessibility to IFAS through Conference
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".