Pregnant mothers have limited knowledge and poor dietary diversity practices, but favorable attitude towards nutritional recommendations in rural Ethiopia: evidence from community-based study
Bibliographic record
Abstract
BACKGROUND: Mothers' nutrition is crucial for good pregnancy outcomes and in improving children's nutritional status. The present study aimed to examine the level of knowledge and attitude towards maternal nutrition and dietary diversity practices among pregnant mothers in rural central Ethiopia. METHODS: In-depth analysis of data from a prospective study involving a total of 389 eligible pregnant women, enrolled during their second antenatal care (ANC) visit was conducted between August 2014 and March 2015. Study participants were selected by employing systematic sampling techniques. Dietary diversity practices were assessed by asking each individual pregnant woman to provide a single 24-h dietary recall. Simple frequencies and graphs were used to present the analyzed data and interpretations. RESULTS: Vegetables were listed top as major sources of vitamin A (45.5%) and iron (23.8%). Nearly half (47%) of the mothers lacked awareness on balanced and diversified diets. Conversely, nearly three fourths (73.8%) and two thirds (66.8%) of them had favorable attitudes towards dietary diversity and early initiation of antenatal care follow up. With a median dietary diversity score of four, starchy staples (100%), legumes and nuts (89.2%) were major food groups consumed by almost all of the mothers included in the study. CONCLUSION: Though pregnant mothers had limited knowledge and poor dietary diversity practices, they exhibited a relatively favorable attitude towards major nutritional recommendations. Use of antenatal care and its follow up as a point of entry for educating pregnant women and increasing nutrition knowledge and attitude is recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".