High Prevalence of Anemia and Poor Compliance with Preventive Strategies among Pregnant Women in Mwanza City, Northwest Tanzania: A Hospital-Based Cross-Sectional Study
Bibliographic record
Abstract
Anemia in pregnancy is prevalent in Tanzania despite the implementation of existing prevention strategies. This study aims to determine the level of compliance with anemia preventive strategies among pregnant women and the factors associated with poor compliance. A cross sectional study was conducted among 768 pregnant women who attended the Bugando Medical Center, Sekou-Toure Regional Hospital, Nyamagana District Hospital, and Buzuruga Health Center in Mwanza, Northwest Tanzania. The prevalence of anemia at term was 68.8% (95% CI, 65.5-72.0%). The average hemoglobin level at term was 10.0 g/dL (95% CI, 9.8-10.1). Only 10.9% of pregnant women complied fully with anemia-preventive strategies. A decrease in mean hemoglobin level was observed across levels of compliance, with women who were non-compliant displaying a significantly lower mean hemoglobin level (8.3 g/dL) compared to women who were fully compliant (11.0 g/dL). Poor compliance was associated with no formal or primary education and initiating antenatal care in the 2nd or 3rd trimester. Anemia in pregnancy was commonly associated with lack of compliance with preventive strategies among participants. There is a need for community-based health education on the importance of complying with anemia-preventive strategies in order to reduce the burden during pregnancy and the consequences of anemia to the unborn baby.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".