Role of Management Functions in Stunting Coverage among Nutrition Officers in Majalengka Regency
https://doi.org/10.33860/jbc.v7i2.4235
Keywords:
stunting, management function, Implementation, supervision, nutrition officerAbstract
Background: Stunting remains a major public health problem in Indonesia, with persistently high prevalence despite ongoing national reduction programs. Primary healthcare centers (Puskesmas) play a frontline role in stunting prevention, yet challenges in management functions such as planning, implementation, and supervision may affect program outcomes. Methods: A cross-sectional study was conducted from July to August 2024 involving all 41 nutrition officers at Puskesmas in Majalengka Regency through total sampling. Data on planning, implementation, and supervision functions were collected using a validated questionnaire, while stunting coverage was obtained from official Puskesmas reports. Variables were categorized into “good” and “poor” based on mean scores. Statistical analysis included Chi-square tests and binary logistic regression to identify factors associated with stunting coverage. Results: More than half of respondents demonstrated poor planning (53.7%), poor implementation (51.2%), and poor supervision (61.0%). Bivariate analysis showed significant associations between all management functions and stunting coverage (p < 0.05). Multivariate logistic regression revealed that planning (OR = 6.9; 95% CI = 1.5–31.7; p = 0.01) and implementation (OR = 5.2; 95% CI = 1.1–23.7; p = 0.03) were significant predictors, with planning emerging as the strongest factor. Conclusion: Planning and implementation functions are critical determinants of stunting program coverage at the Puskesmas level. Weaknesses in technical capacity, human resources, and cross-sectoral collaboration hinder program effectiveness. Strengthening participatory planning, enhancing community engagement, and improving supervision mechanisms are essential strategies to optimize stunting reduction efforts. Further studies with larger and more diverse samples are recommended to validate these findings.
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