Synthetic Remote Work & Mental Health Relationship Dataset

Dataset Features

• Age: Age of the individual in years.
• Gender: Gender of the individual (Male/Female/Prefer not to say).
• Job_Role: Job title or primary role of the individual (e.g., Marketing, Sales, Designer).
• Industry: Industry sector where the individual is employed (e.g., Finance, Education, Retail).
• Years_of_Experience: Total years of professional work experience.
• Work_Location: Current work setting (Remote/Onsite).
• Hours_Worked_Per_Week: Average number of hours worked per week.
• Number_of_Virtual_Meetings: Number of virtual meetings attended weekly.
• Work_Life_Balance_Rating: Self-reported rating of work-life balance (1 = Poor, 5 = Excellent).
• Stress_Level: Stress level of the individual (Low/Medium/High).
• Mental_Health_Condition: Presence of a diagnosed mental health condition (e.g., Anxiety, Depression, Burnout).
• Access_to_Mental_Health_Resources: Whether the individual has access to mental health resources at work (Yes/No).
• Productivity_Change: Change in productivity level due to remote or onsite work (Increase/Decrease/No Change).
• Social_Isolation_Rating: Rating of perceived social isolation (1 = Low, 5 = High).
• Satisfaction_with_Remote_Work: Satisfaction with remote work arrangements (1 = Unsatisfied, 5 = Highly Satisfied).
• Company_Support_for_Remote_Work: Frequency of company-provided support for remote work (None/Weekly/Daily).
• Physical_Activity: Level of physical activity reported (None/Weekly/Daily).
• Sleep_Quality: Self-reported quality of sleep (Poor/Average/Good).
• Region: Geographic region where the individual resides (e.g., Europe, South America, Asia).

Distribution

Usage

• Mental Health Research: Analyze relationships between stress levels, mental health conditions, and organizational support in remote or onsite work settings.
• Productivity Analysis: Explore how remote work affects productivity based on work-life balance, virtual meetings, and stress levels.
• Organizational Policy Design: Develop data-driven workplace policies to support employees' mental health and satisfaction.
• Social Isolation Studies: Investigate the impact of remote work on social connectedness and isolation.
• Health and Wellness Promotion: Examine the role of physical activity, sleep quality, and access to mental health resources in employee well-being.

Coverage

License

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks related to mental health and workplace dynamics.
• Psychologists and Researchers: To explore trends in workplace mental health and employee well-being.
• Human Resources Professionals: To design evidence-based interventions for improving work-life balance and employee satisfaction.
• Public Health Analysts: To study the effects of remote work on mental health at a population level.
• Policy Makers and Regulators: For data-driven decision-making to promote mental health and productivity in remote or hybrid workplaces.