The Dietary Intake of Children Aged 6 to 59 Months and their Hemoglobin Concentration, Central Highland Ethiopia, Community Based Baseline Data
Bibliographic record
Abstract
Iron deficiency anemia (IDA) in young children has lacked attention and priority in developing countries where illiteracy is the greatest encumbrance in the community. This study aimed to assess the hemoglobin level (Hgb) and linear growth of children for baseline data for optional intervention. Methods: Community-based cross-sectional study was carried out among women and their paired children. A multistage sampling method was involved in selecting the study area and 1012 mothers/caregivers and their paired children. Interviewed, blood samples, and anthropometry data were collected and analyzed using IBM SPSS Statistics version 21. Statistical significance was declared at P<0.05 Results: All mothers with their paired children participated in the study. A high proportion (76.7%, n=776) of women aged 20 to 35 years and 505 (59.9%) of women had an awareness of IDA. The mean Hgb concentration of children was 128.23g/L (+ 17.3), and 184 (18.4 %) of the children had anemia, which was higher (24.1%) among age groups 6 -23 months. The highest proportion (42.1%, n = 426) of the children had stunted growth (Height for Age [HFA] Z score < -2 Standard Division [SD]) and a very high prevalence (24.6%, n = 251) of wasting (Weight for Height [WFH] Z score >-1SD) verified among children. The age of children is positively associated with Hgb level (β = 0.172, CI=0.01, 0.33). For a one-month increase in age, Hgb concentration increased by 0.170 mg/del. Conclusions: A very high growth defect and moderate IDA were observed among study subjects. Attentive intervention approaches are important in self-monitoring and a routine modification of used household foods in complementary feed with efficient iron nutrients to reduce growth defects.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".