School feeding program has resulted in improved dietary diversity, nutritional status and class attendance of school children
Bibliographic record
Abstract
BACKGROUND: School Feeding Program (SFP) is a targeted safety net program designed to provide educational and health benefits to vulnerable children. However, limited evidence exists regarding the effect of the intervention on the nutritional status and school attendance of children. The study is aimed at examining the effects of SFP on dietary diversity, nutritional status and class attendance of school children in Boricha district, Southern Ethiopia. METHODS: The study was conducted based on a representative data collected from 290 students drawn from the district. A school-based comparative cross-sectional study was conducted on school children aged 10-14 years. Data were collected using structured pretested questionnaire. The effects of SFP on dietary diversity score (DDS), class attendance rate, body-mass-index for age (BAZ) and height-for-age (HAZ) Z-scores were assessed using multivariable linear regression model. RESULTS: The finding showed significantly higher mean (±SD) of DDS in SFP beneficiaries (5.8 ± 1.1) than the non-beneficiaries (3.5 ± 0.7) (P < 0.001). BAZ and HAZ of the beneficiaries were also higher than their counterparts, which were (0.07 ± 0.93), (- 0.50 ± 0.86) and (- 1.45 ± 1.38), (- 2.17 ± 1.15) respectively (P < 0.001). The mean (±SD) days of absence from school for non-beneficiaries (2.6 ± 1.6) was significantly higher than that of the beneficiaries (1.3 ± 1.7) (P < 0.05). CONCLUSION: Given the positive effects of the program in improving the DDS, nutritional status, and class attendance of school children, we strongly recommend scaling up the program to other food insecure areas.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".