Prevalence and factors associated with mother and newborn skin-to-skin contact in Afghanistan
Bibliographic record
Abstract
BACKGROUND: Mother-newborn skin-to-skin contact (SSC) involves placing the naked infant on the mother's bare chest within the first hour of birth and is crucial for thermoregulation, bonding, breastfeeding initiation, and promoting neonatal health. This study examined the prevalence, and factors associated with SSC in Afghanistan. METHODS: Data from the Afghanistan Multiple Indicator Cluster Survey (MICS) 2022-23 were used and analysed from ever-married women, aged 15-49 years, who delivered a live infant in the past 2 years. The outcome was SSC, placing the naked infant on the mother's bare chest and initiating breastfeeding within the first hour of birth. Adjusted odds ratios [AOR: (95%CI)] of factors associated with SSC were obtained by a logistic regression model. RESULTS: Of 11,992 women, 32.9% practiced SSC. The likelihood of SSC was greater in women with primary [1.38 (1.14-1.68)] and secondary or higher [1.29 (1.06-1.57)] education, in women who had access to media [1.36 (1.11-1.65)], and those who owned mobile phones [1.27 (1.11-1.45)]. The likelihood of SSC was lower in women who delivered at home [0.26 (0.21-0.33)], those who delivered at private clinics or hospitals [0.50 (0.41-0.61)], and those with cesarean section [0.12 (0.08-0.17)]. Women living in rural areas, and women with deliveries conducted by traditional birth attendants/community healthcare workers and by relatives/others had lower odds of SSC [0.76 (0.63-0.92), 0.37 (0.27-0.53), 0.45 (0.33-0.59), respectively]. CONCLUSION: The low prevalence of SSC in Afghanistan highlights the need for targeted health interventions. Efforts should focus on improving access to public clinics and hospitals, enhancing education, training of healthcare providers, and leveraging media and mobile phone access to promote SSC. Interventions should prioritize rural women and women who have undergone cesarean sections to increase SSC rates and improve neonatal health outcomes.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".