Missed opportunities in antenatal care for improving the health of pregnant women and newborns in Geita district, Northwest Tanzania
Bibliographic record
Abstract
BACKGROUND: Despite the significant benefits of early detection and management of pregnancy related complications during antenatal care (ANC) visits, not all pregnant women in Tanzania initiate ANC in a timely manner. The primary objectives of this research study in rural communities of Geita district, Northwest Tanzania were: 1) to conduct a population-based study that examined the utilization and availability of ANC services; and 2) to explore the challenges faced by women who visited ANC clinics and barriers to utilization of ANC among pregnant women. METHODS: A sequential explanatory mixed method design was utilized. Household surveys that examined antenatal service utilization and availability were conducted in 11 randomly selected wards in Geita district. One thousand, seven hundred and nineteen pregnant women in their 3rd trimester participated in household surveys. It was followed by focus group discussions with community health workers and pregnant women that examined challenges and barriers to ANC. RESULTS: Of the pregnant women who participated, 86.74% attended an ANC clinic at least once; 3.62% initiated ANC in the first trimester; 13.26% had not initiated ANC when they were interviewed in their 3rd trimester. Of the women who had attended ANC at least once, the majority (82.96%) had been checked for HIV status, less than a half (48.36%) were checked for hemoglobin level, and only a minority had been screened for syphilis (6.51%). Among women offered laboratory testing, the prevalence of HIV was 3.88%, syphilis, 18.57%, and anemia, 54.09%. In terms of other preventive measures, 91.01% received a tetanus toxoid vaccination, 76.32%, antimalarial drugs, 65.13%, antihelminthic drugs, and 76.12%, iron supplements at least once. Significant challenges identified by women who visited ANC clinics included lack of male partner involvement, informal regulations imposed by health care providers, perceived poor quality of care, and health care system related factors. Socio-cultural beliefs, fear of HIV testing, poverty and distance from health clinics were reported as barriers to early ANC utilization. CONCLUSION: Access to effective ANC remains a challenge among women in Geita district. Notably, most women initiated ANC late and early initiation did not guarantee care that could contribute to better pregnancy outcomes.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".