Exclusive breastfeeding practices in Afghanistan: evidence from the 2022–2023 multiple indicator cluster survey
Bibliographic record
Abstract
BACKGROUND: National prevalence estimates of exclusive breastfeeding practices could serve as the basis for future policy efforts and specific interventions. However, little is known about the prevalence and factors associated with exclusive breastfeeding practices in Afghanistan. This study aims to determine the prevalence and factors associated with exclusive breastfeeding practices among infants aged 0-5 months in Afghanistan. METHODS: Multiple Indicator Cluster Survey (MICS) data collected between 2022 and 2023 were used for this analysis. Data from 3,141 mother-infant dyads were included in the study. The outcome variable was exclusive breastfeeding (EBF), defined as the proportion of infants 0-5 months of age who were fed only breast milk in the past 24 h. Binary logistic regression models were applied to examine the likelihood of EBF across the categories of independent variables. RESULTS: In the studied population, 67.0% (95%CI 65%-69%) of the infants were exclusively breastfed. The likelihood of EBF was higher in infants born to mothers with secondary or higher education [AOR = 1.35, 95%CI 1.04-1.76] and in infants with timely initiation of breastfeeding [AOR = 1.25, 95%CI 1.07-1.46]. However, the female sex of the infant was associated with lower odds of EBF practices [AOR = 0.83, 95%CI 0.72-0.97]. CONCLUSION: The practice of exclusive breastfeeding is at a good level (67%) in Afghanistan. Higher maternal education level, timely breastfeeding initiation, and being a male infant increased the likelihood of EBF practices. Policy efforts and interventions focused on these factors could enhance EBF practices 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".