Eating practices during pregnancy: perceptions of select Maasai women in Northern Tanzania
Bibliographic record
Abstract
BACKGROUND: Globally, pregnant women are challenged to meet sufficient and necessary dietary intake in order to improve maternal and neonatal outcomes. These challenges are amplified in traditional communities, such as the Maasai, where the historical and cultural practices may further curtail, or impact on this dyad's potential success. The research is intended to enhance understanding of Maasai women's pregnancy and nutrition traditions as well as their beliefs. METHOD: Interviews with 12 pregnant Maasai women, all originally from the (Ngorongoro Conservation Area Authority NCAA) area and have spent most or all of their adult lives in the NCAA, sought to answer two research questions: how do these women describe their current dietary pattern and what do they believe is the role of nutrition during pregnancy. RESULTS: Interpretive description methodology was used to reveal five themes: (1) Eating less food makes baby come easier, (2) Not producing food means more dependence, (3) Working hard harms my baby, (4) Knowing what is needed for a good pregnancy and (5) Preferring our traditional ways for pregnancy and birth. CONCLUSIONS: There is an imperative to address nutrition throughout the perinatal period within the Maasai population and the women recognize how important nutrition is for them and their babies. Opportunities to incorporate cultural values and practices must be embedded in programmes/services to achieve success and sustainability. It is important for future prenatal programming with the Maasai in northern Tanzania and other vulnerable groups of pregnant women to build on the women's knowledge of what leads to good 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".