Minimum acceptable diet and contributing factors among children aged 6–23 months in Afghanistan: insights from the 2022–2023 Multiple Indicator Cluster Survey
Bibliographic record
Abstract
BACKGROUND: Undernutrition among children is a public health concern in most low and middle-income countries (LMICs) and is associated with poor child growth and development. Knowledge about child feeding practices is needed for nutritional policies and programs. Hence, this study assessed the status of minimum acceptable diet (MAD) and its associated factors among children aged 6-23 months in Afghanistan. METHODS: This cross-sectional study was based on a secondary dataset of the 2022-2023 Afghanistan Multiple Indicator Cluster Survey (MICS 2022-23). Complete data from 7,876 children aged 6-23 months were analysed. The outcome variable was MAD and was defined according to the WHO and UNICEF recommendations and indicators for young child feeding practices. Bivariate and multivariate binary logistic regression analyses were used to identify factors associated with MAD. RESULTS: About 7.3% of children aged 6-23 months were fed with the recommended MAD. The likelihood of receiving MAD was higher in children aged 13-18 months [adjusted odds ratio (AOR) 2.01 (95%CI: 1.63-2.48)] and 19-23 months [2.11 (95%CI: 1.68-2.66)], in children belonging to households with higher wealth status [1.39 (95%CI: 1.04-1.87), 2.06 (95%CI: 1.51-2.82), and 3.07 (95%CI: 2.14-4.40) for the 3rd, 4th, and 5th quintile of wealth status, respectively], and in children living in rural areas [1.56 (95%CI: 1.21-2.01)]. On the other hand, the maternal age group 30-39 years [0.79 (95%CI: 0.64-0.96)] and non-institutional delivery [0.67 (95%CI: 0.54-0.83)] were associated with reduced odds of MAD. CONCLUSION: Our study revealed that a small percentage (7.3%) of children received MAD in Afghanistan. This emphasizes the need for policies and interventions aimed at the improvement of child feeding practices to ultimately lead to better child nutrition and health in Afghanistan.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".