"Social Marketing" for Early Neonatal Care: Saving Newborn Lives in Pakistan
Bibliographic record
Abstract
According to the World Health Organization and the United Nations Children's Fund, developing countries carry a large share of neonatal mortality in the world. According to UNICEF, almost 450 newborn children die every hour, mostly from preventable causes. Restricted access to quality and hygienic delivery services and limited knowledge about handling the newborn aggravate the situation. South Asia, and Pakistan in particular, have reduced their child and infant mortality during the last decade; however, neonatal mortality still remains unacceptably high. There are multiple reasons, mainly related to practices and behaviours of communities and traditional birth attendants. Rural and poor populations suffer most in Pakistan, where three out of five deliveries still occur at home. Traditional community practices and conservative norms drastically affect neonatal health outcomes. Preventing sepsis at the umbilical cord, keeping the baby at the correct temperature after birth and early initiation of exclusive breastfeeding are three simple strategies or messages that need to be disseminated widely to prevent many neonatal mortalities and morbidities. Since inappropriate practices in handling newborns are directly linked with persistent and unremitting behaviours among health providers and the community at large, we suggest doing robust "social marketing" for saving newborn lives. The objective of the paper is to present a social-marketing strategy and a marketing mix that will help address and surmount actual barriers and promote alternative behaviours in early neonatal care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".