Child Stunting and Maternal Undernutrition in Two Rural Ethiopian Communities Five Years after the Initiation of the National Nutrition Program
Bibliographic record
Abstract
Addressing maternal and child undernutrition is one of the strategic objectives and a priority area agenda for the National Nutrition Program of Ethiopia. We estimated the burden of maternal and child undernutrition in two rural communities (Halaba & Zeway) of Ethiopia and compared outcomes with regional/national reports as well as explored their associations with factors such as gender, socioeconomic‐demographic factors and access‐utilization of health services. In a cross‐sectional study between March and June 2013, we used interviewer administered questionnaire to collect data from mothers in addition to various anthropometric measurements from both mothers and their <5yrs of age children in rural communities of Halaba, south Ethiopia, and Zeway, Oromiya region (n=630 mother‐child pairs, total). Findings showed that maternal undernutrition (% BMI<18.5) ranged from moderate (14%, Zeway) to high (22%, Halaba). We also found alarming levels of stunting and underweight (54% stunting, 36% underweight, in Halaba) and (42% stunting, 21% underweight, in Zeway) among children. Up to 95% of Halaba and 85% of Zeway mothers reported consumption patterns that were ‘same as usual’ or ‘less than usual’ during their most recent pregnancy compared to times of none‐pregnancy/lactation. Up 61% also reported abstaining from consumption of certain nutritious foods for cultural reasons. Factors such as gender and socio‐economic‐demographic structure of the household, including imbalance of power, physiological density, household size and dietary habits during pregnancy showed significant associations with maternal and child undernutrition ( p <0.05), warranting further investigation. The observed levels of child and maternal undernutrition, particularly in Halaba areas were unexpected and of serious concern, given that a national nutrition program administered by the government has been in place for some time. This baseline study provides insights to policy and decision makers to revise and/or strengthen the nutrition programs designed to target vulnerable segments of the population in these regions. Support or Funding Information The research was supported by the International Development Research Centre (IDRC) and the Department of Foreign Affairs Trade and Development (DFATD), Government of Canada, through the Canadian International Food Security Research Fund (CIFSRF).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".