Explanation of the Conceptual Model for Enhancing Users’ Mental Health in Therapeutic Spaces Based on Clustering Environmental Factors
Keywords:
Mental health, environmental characteristics, clustering, machine learningAbstract
The quality of the inpatient environment, alongside medical services, is recognized as an important factor influencing the speed of patient recovery and their mental health during hospitalization. The purpose of this study is to present a conceptual framework based on the clustering of environmental characteristics that affect patients’ recovery during their hospital stay. To this end, a literature review was conducted using three databases—Web of Science and Scopus—which ultimately resulted in the selection of 20 articles published over the past ten years. The method for classifying the characteristics was based on a clustering approach grounded in machine learning. Despite the lack of clarity regarding causal relationships, substantial evidence indicates that inpatient environments influence patients’ health outcomes. This article proposes a conceptual framework that links various interior environmental characteristics of therapeutic spaces to users’ health outcomes. The framework provides a structure for grouping causal effects based on their association with three general spatial features: a comfortable environment, a calming space, and good spatial performance in relation to the interior environment of therapeutic spaces.
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