Scientifically Grounded Model for Managing and Evaluating Community Health-Promotion Activities at the Mahalla Level
Abstract
This paper presents a scientific framework for evaluating and managing health-promotion activities at the community (mahalla) level. While human health and the promotion of healthy lifestyles are recognized as global priorities, local-level monitoring faces serious challenges such as fragmented data, inconsistent indicators, and limited forecasting. To address these gaps, the study applies a parametric approach that combines statistical indicators with expert assessments. Weighting coefficients, ranking, and scoring methods are employed to generate integral evaluations. The results show that assessments without weighting fluctuate widely and are unreliable for decision-making. By contrast, the ranking method provides the most stable results, while the scoring method offers moderate accuracy with practical value. Compared with previous research focused mainly on statistical monitoring, this approach integrates analytical methods, enabling deeper evaluation and limited forecasting of health-promotion processes. Although some limitations remain, including reliance on available statistics and subjective expert input, the proposed model enhances evidence-based decision-making. Practically, it supports effective monitoring, resource allocation, and early detection of problems, thereby contributing to improved community well-being and the development of a healthy society. Future improvements may involve machine learning and the inclusion of ecological, psychological, and cultural indicators to increase both accuracy and applicability.