Predictors of the amount of intake of Ready‐To‐Use‐Therapeutic foods among children in outpatient therapeutic programs in Nairobi, Kenya
Bibliographic record
Abstract
Ready-to-use Therapeutic Food (RUTF) therapy is a standard protocol for treating children with severe acute malnutrition (SAM) admitted in Out-Patient Therapeutic Programmes (OTP). The amount of RUTF to be consumed by a child is based on weight (200 kcal/kg body weight/day) as stipulated in the Kenya Integrated Management of Acute Malnutrition (IMAM) protocol for timely weight gain. There is limited information on the determinants of consumption of the correct amount of RUTF. This study sought to fill this gap by establishing the associations between the caregivers' and the child's characteristics and the amount of RUTF the child ate within a 24-h recall period. We used a cross-sectional study design and interviewed 200 caregivers of children 6-23 months of age admitted in four OTP centers in Nairobi Kenya. We used a researcher-administered questionnaire to collect information from the caregivers. Seventy-three percent of the children ate the recommended amount of RUTF. A smaller proportion (54.4%) of younger children (6-11 months of age) ate the recommended amount of RUTF compared to older children (12-17 months old and 18-23 months old at 89.1% and 82.8%, respectively). The predictors of consumption of the correct amount of RUTF were child's birth order-firstborn (AOR 29.92; 95% CI: 5.67-157.93) and children's age; 12-17 months old (AOR 5.19; 95% CI: 2.18-12.36) and 18-23 months (AOR 6.19 95% CI: 2.62), indicating that firstborn and older children were more likely to consume the correct amounts of RUTF. Caregivers' knowledge and correct practices in feeding a child with RUTF also predicted the consumption of the correct amount of RUTF. In conclusion, maternal and child characteristics are determinants of the consumption of the correct amount of RUTF by children in OTP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".