Factors associated with delayed neonatal bathing in Afghanistan: insights from the 2022–2023 multiple indicator cluster survey
Bibliographic record
Abstract
OBJECTIVES: Delayed neonatal bathing, defined as postponing the first bath until at least 24 h after birth, is a key component of essential newborn care that helps maintain thermal stability and reduces the risk of hypothermia and infection. This study estimates the national prevalence of delayed neonatal bathing and identifies its determinants in Afghanistan. This study analyzed data from the Afghanistan Multiple Indicator Cluster Survey (MICS) 2022-2023. We fitted multivariable binary logistic regression models to determine factors associated with delayed neonatal bathing. RESULTS: Out of 7,702 women, 68.6% reported delayed neonatal bathing. After adjustment, the odds of delayed bathing were higher among women whose household head completed primary education (AOR 1.38; 95% CI: 1.10-1.73), those delivering in health facilities (AOR 1.57; 95% CI: 1.29-1.91), and women attending 1-3 antenatal care (ANC) visits (AOR 1.29; 95% CI: 1.08-1.53) or 4-7 ANC visits (AOR 1.40; 95%CI: 1.14-1.72) or ≥ 8 ANC visits (AOR 2.05; 95% CI: 1.46-2.87). Conversely, women in the richest wealth quintile were less likely to delay bathing (AOR 0.69; 95% CI: 0.51-0.94). Tailored interventions that leverage antenatal contacts and facility-based care may further improve the adoption of optimal newborn bathing 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".