Title
Author
DOI
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Special Issue
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Deadline for manuscript submissions: 31 May 2025
Print Special Issue Flyer (13)Prof. Youngju Jee, PhDE-MailWebsite
Department of Nursing, Kyungnam University, Republic of Korea
Interests: Health care; Health promotion; Occupational health; Child Health; Elderly health/geriatric health
Prof. Haewon Byeon, PhDE-MailWebsite
Department of Digital Anti-aging Healthcare, Inje University, Republic of Korea
Interests: Aging and prediction model; Quality of life; Mental health; Cognitive frailty
Dear Colleagues,
I would like to extend an invitation to you to contribute to a special issue of the Journal of Men's Health dedicated to the exploration, diagnosis, and management of mental health issues among male workers. Mental health disorders such as depression, anxiety disorders, panic disorders, post-traumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD) are significant public health concerns that particularly impact male workers worldwide.
Male workers often face unique stressors related to their work environment, job security, and societal expectations, which can exacerbate mental health issues and hinder their willingness to seek help. This special issue aims to shed light on the specific challenges faced by male workers in dealing with mental health disorders and to promote research that can lead to improved diagnosis, management, and prevention strategies tailored to this population.
Depression and anxiety disorders are among the most prevalent mental health conditions in male workers. Studies indicate that men in the workforce are less likely than their female counterparts to seek help for these conditions, which can lead to severe consequences such as decreased productivity, substance abuse, and even suicide. PTSD and panic disorders are also common, particularly among male workers exposed to high-stress environments or traumatic events. OCD, though less common, significantly impacts the quality of life and job performance of affected individuals.
Recent research suggests that a combination of workplace stress, societal expectations, and psychological factors contribute to the onset and progression of these disorders in male workers. For instance, long working hours, job insecurity, and work-life imbalance can exacerbate mental health issues. Additionally, the COVID-19 pandemic has highlighted the importance of mental health, with increased rates of anxiety, depression, and stress observed among the workforce globally.
This special issue seeks to advance our understanding of the mechanisms underlying mental health disorders in male workers, with a focus on modifiable factors such as workplace environment, work-life balance, and health behaviors. We welcome original research articles, reviews, systematic reviews, and meta-analyses that address the following topics, among others:
• Predictive modeling of depression, anxiety, PTSD, panic disorder in male workers;
• Diagnosis and management of depression, anxiety, PTSD, panic disorder, and OCD in male workers;
• Impact of workplace and cultural factors on mental health in male workers;
• Strategies to reduce stigma and improve mental health care access for male workers;
• Role of workplace interventions and policies in the prevention and management of mental health disorders;
• Relationship between working environment, hearing loss, and depression;
• Effect of the COVID-19 pandemic on the mental health of male workers.
By addressing these topics, we hope to provide comprehensive insights and practical recommendations for improving mental health outcomes in men. We encourage submissions that utilize diverse methodologies, including observational studies, cross-sectional analyses, prospective cohort studies, case-control studies, and randomized clinical trials.
The submission deadline for this special issue is 2025/5/31. We look forward to your valuable contributions to this important area of men's health.
Yours sincerely,
Dr. Prof. Haewon Byeon, Prof. Youngju Jee
Workplace factors; Work-life balance; Prevention; Prediction; High-risk group; Quality of life; Depression; Anxiety; PTSD; Panic disorder; OCD; Diagnosis; Management; Mental health; Societal factors; Lifestyle
Manuscripts should be submitted online by submit system. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Original articles, case reports or comprehensive reviews are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. JOMH is an international peer-reviewed open access journal published by MRE Press. As of January 2021, JOMH will change to a monthly journal. Please visit the Instructions for Authors page before submitting a manuscript.The Article Processing Charge (APC) for publication in this open access journal is $2000. We normally offer a discount greater than 30% to all contributors invited by the Editor-in-Chief, Guest Editor (GE) and Editorial board member. Submitted papers should be well formatted and use good English.
Predictions of Discontent in Life of Male Migrant Workers in Korea
Predictive Model of Job Satisfaction of Disabled Male Workers Using Explainable Artificial Intelligence
Life Satisfaction Prediction Model of Male Workers with Disabilities Using LIME Algorithm
Machine Learning-Based Prediction Model of Depression in Male Office Workers
Mental Health Prediction of Male Workers Based on Regression Model
Predicting Factors Influencing Suicidal Ideation Among Middle-Aged Male Wage Workers in South Korea: A Study Using Stochastic Gradient Descent Regressor and Logistic Regression Analysis
A Fusion Model of Robust and Sparse Twin Support Vector Machine and Logistic Regression for Predicting Occupational Musculoskeletal Disorders Among South Korean Male Office Workers
Anxiety Disorder Prediction Model for Male Workers Based on Relational Attention Relational Neural Networks
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