Early Childhood Nutrition Knowledge of Caregivers in Tanzania
Bibliographic record
Abstract
Childhood stunting is a pressing health issue in Tanzania and results from chronic infections and inadequate nutrition. Educating caregivers on the nutritional determinants, their consequences, and appropriate solutions may improve nutrition-related practices among caregivers in Tanzania. The purpose of this study was to identify factors associated with Tanzanian caregivers’ knowledge of childhood nutrition practices. Data for this study came from a cross-sectional survey of 4,095 caregivers of children under 24 months living in the Geita, Kagera, Kigoma, Mwanza, and Shinyanga regions of Tanzania. Complete responses relating to demographic and socioeconomic factors, media exposure, and early childhood nutrition knowledge were analyzed using multiple linear regression modeling techniques. Caregivers’ knowledge concerning proper early childhood nutrition practices was found to be significantly related to using a mobile banking account (p<.0001), owning a working radio with batteries (p<.0001), having watched television recently (p<.0001), residing in a southern lake region (p<.0001), affiliating with a Christian faith (p=0.0027), having more children under the age of 5 (p=0.0005), having received advice on maternal nutrition before pregnancy (p<.0001) and having received advice from a community health worker (p=0.0184). Living in a rural environment (p<.0001) and speaking a non-mainstream language (p<0.05) were significantly associated with decreased knowledge. The influences of media and technology, socio-demographic factors and traditional health education may be important in the development of accurate childhood nutrition knowledge among caregivers. These factors may be targeted for future community health worker efforts with vulnerable populations in Tanzania to prevent stunting.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".