Scientometric Mapping of Nutritional Policy Governance and Logistic Regression Modelling of Malnutrition Determinants in the Post-Pandemic Era
DOI:
https://doi.org/10.59680/ishel.v3i1.2256Keywords:
Food Systems, Logistic Regression, Malnutrition Determinants, Nutritional Governance, Public Health AdministrationAbstract
The post-COVID-19 era has intensified global concerns about malnutrition, food insecurity, and the effectiveness of nutritional policy governance. Disruptions to food systems, healthcare services, and socioeconomic stability have worsened both undernutrition and overnutrition, particularly in low- and middle-income countries. This study integrates scientometric mapping and multivariate statistical modeling to explore the evolution of nutritional policy governance research and identify determinants of malnutrition in the post-pandemic context. A bibliometric dataset of peer-reviewed publications (2010–2024) was analyzed to map thematic clusters, research fronts, and governance paradigms using co-occurrence network analysis and thematic evolution techniques. Additionally, logistic regression modeling was applied to post-pandemic secondary data on nutritional outcomes to assess associations between malnutrition status and socioeconomic, health system, and policy-related factors. The scientometric analysis identifies three dominant research clusters: (1) food system governance and sustainability; (2) social protection and nutrition equity; and (3) the double burden of malnutrition and policy integration. Emerging themes include digital food governance, the climate–nutrition nexus, and pandemic resilience frameworks. Logistic regression results indicate that household income instability, low maternal education, limited access to primary healthcare, and food price inflation significantly increase malnutrition risk. Conversely, broader social protection coverage and community-based nutrition interventions demonstrate protective effects. By combining structural mapping of scholarly development with empirical modeling of risk factors, this hybrid approach offers comprehensive insights into post-pandemic nutritional governance. The findings underscore the importance of multisectoral coordination, adaptive social protection systems, and data-driven monitoring to strengthen nutritional resilience and inform sustainable policy design in the recovery phase.
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