There has been an increase in mental health problems globally, especially during and after the Pandemic since people were isolated for long periods of time. Depression is one of the leading causes of disability, with workplace stress being one of factors that could contribute to mental health problems in adults.
Doing an analysis of this dataset and grouping certain employees together based on their mental state and other work-related factors could allow for insight on which factors are causing high stress levels. It could also help predict which employees are likelier to experience workplace stress for certain things, and see who may need to seek treatment possibly.
If this proves to be successful, employees and employers would reap benefits. Employers would be able to detect workplace stress in a timely manner, which allows employees to get guidance and find ways to rid themselves of this stress. In addition, employers can see commonalities within the factors that cause stress in the overall demographic and adjust certain practices.
This dataset used is the "MXMH Survey Results" from Kaggle (https://www.kaggle.com/datasets/catherinerasgaitis/mxmh-survey-results?select=mxmh_survey_results.csv), and it has data regarding mental health in the workplace. There are responses from 1,427 employees from different orgs. There are many variables in this dataset:
Timestamp: date and time when the survey was completed Age: age of the respondent Gender: gender identity of respondent Country: country of respondent state: state or province of respondent self_employed: if respondent is self-employed family_history: if respondent has a family history of mental illness treatment: if respondent sought treatment mental health work_interfere: if a mental health condition has interfered with respondent's work no_employees: # of employees in respondent's workplace remote_work: if respondent works remotely tech_company: if respondent's employer is a tech company benefits: if respondent's employer offers mental health guidance care_options: mental health care provided by respondent's employer wellness_program: if respondent's employer offers a wellness program seek_help: if respondent knows how to seek help for mental health issues anonymity: if respondent's anonymity would be protected leave: amount of time the respondent feels comfortable taking off work for mental health mental_health_consequence: if respondent's employer has discussed mental health as part of employee's performance review phys_health_consequence: if respondent's employer discussed physical health as part of employee's performance review coworkers: if respondent's comfortable discussing mental health issues with coworkers supervisor: if respondent's comfortable discussing a mental health issue with the supervisor mental_health_interview: if respondent would be comfortable discussing a mental health issue with a potential employer during an interview phys_health_interview: if respondent would be comfortable discussing a physical health issue with a potential employer during an interview mental_vs_physical: if respondent feels mental health is taken as seriously as physical health in the workplace obs_consequence: if respondent's seen negative consequences for coworkers with mental health issues in the workplace comments: extra comments from respondent self_identified_condition: if respondent has a mental health issue that they self-identified work_for_tech: ifrespondent works for a tech company mental_health_interview_career: if respondent has ever had a job interview where potential employer cared about their mental health mental_health_interview_career_consequence: if respondent experienced negative consequences for discussing their mental health during an interview phys_health_interview_career: if respondent had a job interview where potential employer was concerned about their physical health phys_health_interview_career_consequence: if respondent experienced negative consequences for discussing their physical health during an interview. coworkers_discussed: if respondent discussed their mental health with coworkers supervisor_discussed: if respondent discussed their mental health with coworkers etc.. (there are 52 variables)
import pandas as pd
df = pd.read_csv('mxmh_survey_results.csv')
I would cluster employees based on their current mental health issues, or lack thereof. In addition, I would group them based on the job-related factors and this will allow for a deeper look at which factors cause stress and which employees could benefit from guidance. Many different variables can be clustered and grouped together to do so.