Food‐basket intervention to reduce micronutrient deficiencies among Maasai‐pregnant women in Tanzania: a quasi‐experimental study
Bibliographic record
Abstract
BACKGROUND: Micronutrients comprised of vitamin and mineral nutrients that are needed during pregnancy for foetal growth, development and maturation, as well as for reducing/preventing maternal complications. However, micronutrient-rich foods (vegetables and fruits) are lacking in the Ngorongoro Conservation Area as a result of restrictions on cultivation in conservation areas and the unavailability of vegetables and fruits in local markets. The present study introduced a food basket intervention and assessed the effectiveness of the food baskets with respect to addressing anaemia, vitamin A and iron deficiencies among pregnant Maasai women within the Ngorongoro Conservation Area. METHODS: The quasi-experimental study included Misigiyo ward as a control group (provided education only) and Olbalbal ward as an intervention group (provided food baskets and education). The study assessed haemoglobin, serum ferritin and retinol at baseline and during follow-up. Haemoglobin, serum ferritin and retinol were quantitatively (duplicate) measured with HemoCue™ (HemoCue AB, Ängelholm, Sweden), Maglumi 800 (Snibe Diagnostic, Shenzhen, China) and vitamin A enzyme-linked immunosorbent assay, respectively. Dependent and independent t-tests were used to compare the micronutrient blood levels between and within the groups. RESULTS: The present study found a statistically significant increase in serum retinol (P < 0.001) in the intervention group compared to the control group; moreover, baseline serum retinol was positively associated with the follow-up serum retinol, whereas baseline haemoglobin and serum ferritin were negatively associated. CONCLUSIONS: The food basket intervention holds promise with repect to reducing micronutrient deficiency, especially in communities where micronutrient-rich foods are scarce.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".