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Healthcare Staff Stress Data
Mental Health & Wellness
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About
focuses on illuminating the impact of high stress and burnout rates experienced by healthcare professionals. It allows for the examination of key workplace factors, such as heavy workloads, poor work environments, and emotional demands. Furthermore, it facilitates understanding how these stressors influence critical outcomes like job satisfaction, absenteeism, and intentions regarding staff turnover. Please note, this material is synthetically generated and is intended solely for educational use, not for real-world policy implementation or decision-making.
Columns
The data file contains 10 columns, structured as follows:
- Employee ID: A unique string identifier for each of the 5,000 simulated employees.
- Employee Type: String indicating the role, with examples including Registered Nurse (26%) and Medical Assistant (10%).
- Department: String detailing the employee’s department, such as General Medicine (26%) and Outpatient Services (10%).
- Workplace Factor: String identifying the primary stressor, with Heavy Workload (43%) and Poor Work Environment (22%) being common examples.
- Stress Level: Integer scale ranging from 4 to 9, with a mean score of 7.33.
- Burnout Frequency: String describing the regularity of burnout, with ‘Often’ being the most common classification (44%).
- Job Satisfaction: Integer scale ranging from 1 to 5, with a mean score of 2.2.
- Access to EAPs: Boolean indicating whether the employee has access to Employee Assistance Programmes (True 72%, False 28%).
- Mental Health Absences: Integer representing the number of absences, ranging from 0 to 19, with a mean of 7.4 days.
- Turnover Intention: Boolean indicating the intent to leave the job (True 67%, False 33%).
Distribution
The data is provided in a CSV file format (
Healthcare Workforce Mental Health Dataset.csv), measuring 424.84 kB. It contains 10 columns and is structured around 5,000 unique records. The expected update frequency for this dataset is monthly.Usage
This data is ideally suited for educational and analytical applications. It provides a valuable resource for developing data exploration and data visualization skills. Specific use cases include interactive data exploration, deriving workforce insights, and conducting stress factor analysis. It is compatible with analytics tools, often being bundled with a Power BI Dashboard Template for enhanced visualization.
Coverage
The scope covers simulated data points related to the healthcare workforce. This material was developed using probabilistic modelling, Python data science techniques, and guidance validated by AI (ChatGPT), based on industry reports and research from organizations such as the WHO, CDC, and OSHA. No specific geographic or time range is stated as the data is synthetic.
License
CC BY-SA 4.0
Who Can Use It
Intended users include data analysts seeking practical application scenarios, students developing their understanding of data modelling, and career changers looking to practise new skills. Anyone interested in studying staff retention metrics and mental health trends within stressful professional settings will find this resource useful.
Dataset Name Suggestions
- Healthcare Staff Stress Data
- Burnout and Retention Metrics
- Synthetic Healthcare Employee Wellbeing
- Workforce Mental Health Analytics
Attributes
Original Data Source: Healthcare Staff Stress Data
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