Management of Students\' Happiness in Dormitories of Isfahan University of Medical Sciences

Introduction: Happiness is the most fundamental concept in positive-oriented psychology. Students are influential and generating forces that need happy environment and mood. This Study aimed to determine factors affecting happiness of students who live in dormitories of Isfahan University of Medical Sciences Methods: This Study was conducted on 120 male and female students living in dormitories using an open questionnaire. Data were coded and categorized and reported as frequency and percentage. Results: Results showed that the most important factors affecting students' happiness before entering the university, were sports (26/4%), television (13/05%), and walking (11/86%). the most recreation programs after entering university were sports (22/9 %), television and video room (12/84 %), computer and internet (8/33 %). Students suggested some programs to improve conditions and increase the happiness. They were providing some inside and outside camps (15/21%), improving beautify of campus (12/64%) and providing recreational facilities and sports competitions (10/5 %). Conclusion: The findings of this research aimed improving students' happiness can be considered by officials and policy makers of dormitories. This aim will be achieved through expansion of existing facilities and purposeful long-term plannin

A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods

The unequal distribution of healthcare services is the main obstacle to achieving health equity and sustainable development goals. Spatial accessibility to healthcare services is an area of interest for health planners and policymakers. In this study, we focus on the spatial accessibility to four different types of healthcare services, including hospitals, pharmacies, clinics, and medical laboratories at Isfahan’s census blocks level, in a multivariate study. Regarding the nature of spatial accessibility, machine learning unsupervised clustering methods are utilized to analyze the spatial accessibility in the city. Initially, the study area was grouped into five clusters using three unsupervised clustering methods: K-Means, agglomerative, and bisecting K-Means. Then, the intersection of the results of the methods is considered to be conclusive evidence. Finally, using the conclusive evidence, a supervised clustering method, KNN, was applied to generate the map of the spatial accessibility situation in the study area. The findings of this study show that 47%, 22%, and 31% of city blocks in the study area have rich, medium, and poor spatial accessibility, respectively. Additionally, according to the study results, the healthcare services development is structured in a linear pattern along a historical avenue, Chaharbagh. Although the scope of this study was limited in terms of the supply and demand rates, this work gives more information and spatial insights for researchers, planners, and policymakers aiming to improve accessibility to healthcare and sustainable urban development. As a recommendation for further research work, it is suggested that other influencing factors, such as the demand and supply rates, should be integrated into the method

A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods

The unequal distribution of healthcare services is the main obstacle to achieving health equity and sustainable development goals. Spatial accessibility to healthcare services is an area of interest for health planners and policymakers. In this study, we focus on the spatial accessibility to four different types of healthcare services, including hospitals, pharmacies, clinics, and medical laboratories at Isfahan’s census blocks level, in a multivariate study. Regarding the nature of spatial accessibility, machine learning unsupervised clustering methods are utilized to analyze the spatial accessibility in the city. Initially, the study area was grouped into five clusters using three unsupervised clustering methods: K-Means, agglomerative, and bisecting K-Means. Then, the intersection of the results of the methods is considered to be conclusive evidence. Finally, using the conclusive evidence, a supervised clustering method, KNN, was applied to generate the map of the spatial accessibility situation in the study area. The findings of this study show that 47%, 22%, and 31% of city blocks in the study area have rich, medium, and poor spatial accessibility, respectively. Additionally, according to the study results, the healthcare services development is structured in a linear pattern along a historical avenue, Chaharbagh. Although the scope of this study was limited in terms of the supply and demand rates, this work gives more information and spatial insights for researchers, planners, and policymakers aiming to improve accessibility to healthcare and sustainable urban development. As a recommendation for further research work, it is suggested that other influencing factors, such as the demand and supply rates, should be integrated into the method