Introduction: This work aims to explore the impacts of social support measures such as analyzing greening urban spaces and improving greenspace quality on the mental health of the elderly based on the mental resilience framework to build a healthy community.
Methods: Through literature research and a questionnaire survey, this study designs a questionnaire including basic information, a life satisfaction scale, a positive and negative emotion scale, social support rating scale, a life orientation test-revised version, and psychological resilience scale. In order to classify the scales, machine learning (ML) approaches are applied in this work.
Results: The results demonstrate great variations in the scores of the elderly with various health problems in the domain of psychological resilience (p< 0.001). Social support and subjective well-being are strongly related (p< 0.001). Additionally, positive correlations are observed among perceived, objective, and support use (p< 0.001).
Conclusion: Finally, it is found that psychological resilience and social support play a significant role in mediating mental health problems in elderly individuals. Therefore, social support measures such as analyzing greening urban spaces and improving greenspace quality can be adopted to improve the mental health of the elderly to build healthy communities. On this basis, this work is of certain reference value and practical significance for the construction of healthy communities and the positive influence on the mental health of elderly individuals.
Keywords: machine learning, ML, psychological resilience, social support, mental health of elderly individuals, greening urban spaces, greenspace quality, healthy community