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Volume 15, Issue 2 (2023)
Iran J War Public Health 2023, 15(2): 123-131 |
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History
Received: 2023/04/21 | Accepted: 2023/06/12 | Published: 2023/08/1
Received: 2023/04/21 | Accepted: 2023/06/12 | Published: 2023/08/1
How to cite this article
Lorestani F, Zarghami M, Shatrian F, Mosavi B. Mental Health Model of Iranian Veterans with Network Analysis Approach. Iran J War Public Health 2023; 15 (2) :123-131
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1- Department of Psychology, Saveh Branch, Islamic Azad University, Saveh, Iran
2- Behavioral Sience Research Center, Lifestyle Research Institue, Baqiyatullah University of Medical Sciences, Tehran, Iran, Behavioral Sience Research Center, Lifestyle Research Institue, Baqiyatullah University of Medical Sciences, Shikh Bahei, Tehran, Iran. Postal Code: 1435916471 (zar100@gmail.com)
3- Prevention Department, Janbazan Medical and Engineering Research Center, Tehran Iran
2- Behavioral Sience Research Center, Lifestyle Research Institue, Baqiyatullah University of Medical Sciences, Tehran, Iran, Behavioral Sience Research Center, Lifestyle Research Institue, Baqiyatullah University of Medical Sciences, Shikh Bahei, Tehran, Iran. Postal Code: 1435916471 (zar100@gmail.com)
3- Prevention Department, Janbazan Medical and Engineering Research Center, Tehran Iran
Abstract (518 Views)
Aims: The present study aimed to provide a model of the mental health of veterans and identify its determining factors.
Information & Methods: The study method is based on correlational network data analysis. The data of 9244 veterans, including demographic characteristics and data related to mental health, were extracted in the second 6 months of 2020, and then the network indexes were calculated.
Findings: Age, number of children, percentage of veterans, percentage of neurological and mental disorders, physical functioning, bodily pain, and mental health were the variables that had the most strength. Age, number of children, pension, percentage of neurological and mental disorders, physical functioning, depression, anxiety, general health, physical role, physical functioning, social functioning, and vitality were the closeness factors in the mental health network of veterans. The Benit value for the variables of depression, age, mental health, physical role, physical functioning, and the number of children in the mental health network of veterans was non-zero. The variables of number of children, percentage of mental disorders, psychiatric diagnoses, especially depression, employment standby, physical problems, and vitality had the highest eigenvector values in the mental health network of veterans. The variables of the number of children, the percentage of mental disorders, depression, vitality, history of hospitalization, and the type of hospitalization had higher leverage (positive) with the mental health network.
Conclusion: The variables related to quality of life, along with age, number of children, percentage of neurological and mental disorders, and depression, are the main determinants of mental health.
Information & Methods: The study method is based on correlational network data analysis. The data of 9244 veterans, including demographic characteristics and data related to mental health, were extracted in the second 6 months of 2020, and then the network indexes were calculated.
Findings: Age, number of children, percentage of veterans, percentage of neurological and mental disorders, physical functioning, bodily pain, and mental health were the variables that had the most strength. Age, number of children, pension, percentage of neurological and mental disorders, physical functioning, depression, anxiety, general health, physical role, physical functioning, social functioning, and vitality were the closeness factors in the mental health network of veterans. The Benit value for the variables of depression, age, mental health, physical role, physical functioning, and the number of children in the mental health network of veterans was non-zero. The variables of number of children, percentage of mental disorders, psychiatric diagnoses, especially depression, employment standby, physical problems, and vitality had the highest eigenvector values in the mental health network of veterans. The variables of the number of children, the percentage of mental disorders, depression, vitality, history of hospitalization, and the type of hospitalization had higher leverage (positive) with the mental health network.
Conclusion: The variables related to quality of life, along with age, number of children, percentage of neurological and mental disorders, and depression, are the main determinants of mental health.
Keywords:
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