The Role of Mother Empowerment and Macro-Economic Factors for Child Health: An Evidence from Developing Economies
Bibliographic record
Abstract
Objective: To analyzed the role of maternal empowerment and macro-economic variables in the improvement of child health in developing economies. Methodology: Maternal empowerment has measured through five dimensions: work status, awareness, decision making, self-esteem, and self-confidence. Moreover, countries' net foods imports, countries as secular or non-secular and region are selected as macro-economic factors. On the other hand, child health has analysed through the anthropometric measure, i.e. stunting. The most recent data sets of Demographic and Health Surveys (DHS) of 38 countries have been used. Data has been analyzed through the use of binary logistic regression and explore the impact of maternal empowerment and macro-economic factors on child health. Results: The results explain the positive impact of mother empowerment in the improvement of child health. Furthermore, net food imports are positively effecting the child's health. Sub-Saharan Africa and Secular states proved to have negative impacts on child health. Most probably the more empowered mothers are more contributors and implement positive effects on their children’s health. Conclusion: The countries which can fill their food deficiencies through food imports have the probability of improved health for the next generation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".