Mental Health in Tech Workplace

• Repository
• Kaggle Dataset

In this project we'll try to understand what are the factos contributing the mental health of a person. This dataset is from a 2014 survey that measures attitudes towards mental health and frequency of mental health disorders in the tech workplace. There are always a lot of great project regarding different ways of solving the problems but only a few handful address the problems of domain knowledge and getting started. I will built on that to explain the dataset,perform EDA and then Build a baseline model.

Background

What exactly do we mean by Mental Health at workplace?

Mental health affects your emotional, psychological and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices. In the workplace, communication and inclusion are keys skills for successful high performing teams or employees. The impact of mental health to an organization can mean an increase of absent days from work and a decrease in productivity and engagement. In the United States, approximately 70% of adults with depression are in the workforce. Employees with depression will miss an estimated 35 million workdays a year due mental illness. Those workers experiencing unresolved depression are estimated to encounter a 35% drop in their productivity, costing employers $105 billion dollars each year.

What can your employer do about this?

So what can employers do? It’s called Mental Health First Aid. Mental Health First Aid teaches participants how to notice and support an individual who may be experiencing a mental health or substance use concern or crisis and connect them with the appropriate employee resources. It teaches employees critical communication and support skills that can influence your organizations bottom line.

Research shows that employees who go through Mental Health First Aid have an increased awareness of mental health among themselves and their co-workers. It allows them to recognize the signs of someone who maybe struggling and teaches them the skills to know when to reach out and what resources are available. Which in turn creates beneficial intervention that increases engagement and creates an environment of inclusion and support.

Employers can also offer robust benefit packages to support employees who go through mental health issues. That includes Employee Assistance Programs, Wellness programs that focus on mental and physical health, Health and Disability Insurance or flexible working schedules or time off policies.

Organizations that incorporate mental health awareness help to create a healthy and productive work environment that reduces the stigma associated with mental illness, increases the organizations mental health literacy and teaches the skills to safely and responsibly respond to a co-workers mental health concern. Incorporating mental health awareness in the workplace can help lead the way for mental health issues throughout your community by equipping people with the tools they need to start a dialogue so that more people can get the help they need.

Data Fields

Import Package and Data

import warnings 
warnings.filterwarnings('ignore')

#basic libraries

import numpy as np
import pandas as pd

#visualization libraries

import missingno as msno
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
%matplotlib inline

For this exercise, the data set (.csv format) is uploaded to my repository github, read into the Jupyter notebook and stored in a Pandas DataFrame.

Data Preparation and Cleaning

Checking some feature from dataset

There are a total of 26 columns in the dataset. We see that except the age column, all the columns are of object datatype. Comment column seems to contain most number ( 70% ) of null values, which makes sense because it was an optional text box so it's reasonable to expect that many (most) respondents would leave it blank. We will be dropping the timestamp column because it's contains date, month, year and time the respondent took this questionnaire, which is irrelevant for us. The state column also contains a lot of null values. We'll dig deeper into that.

It will be really misleading to conclude that a certain country faces more problem with the mental health of employees because around 60% of the people belong to The US. Moreover there are a lot of countries which have only one respondents. The country column thus becomes pointless. We will be dopping this. A quick look at the states suggest us that it is applicable for the one's only in The US, so we'll drop it as well.

Unknowingly, we have stumbled upon the fact that the number of males in the dataset are 4 times the number of females. Thus, we must keep this in mind and avoid making any faulty assumptions that males are more susceptible to mental health issues etc. Alternatively, we may conclude that the number of males in the tech industry are much more as compared to the number of females (This research was conducted specifically for the tech industry).

There's only one column which is 'work_interfere' remaining that contains null values. For now we will proceed without any imputation. Actually, there's another column, 'self_employed' which contains around 18 null values which we failed to notice at first.

Exploratory Data Analysis

Before begining with the EDA which should learn about the organization which has collected this data. Open Sourcing Mental Illness is a non-profit, corporation dedicated to raising awareness, educating, and providing resources to support mental wellness in the tech and open source communities. OSMI began in 2013, with Ed Finkler speaking at tech conferences about his personal experiences as a web developer and open source advocate with a mental health disorder. The response was overwhelming, and thus OSMI was born.

This is the respondents result of question, 'Have you sought treatment for a mental health condition?'

This is our target variable. Looking at the first graph, we see that the percentage of respondents who want to get treatment is exactly 50%. Workplaces that promote mental health and support people with mental disorders are more likely to have increased productivity, reduce absenteeism, and benefit from associated economic gains. If employees enjoy good mental health, employees can:

This is respondent's answer to the question, 'Are you self-employed?'.

We see that the number of people who are self employed are around 10%. Most of the people who responded to the survey belonged to working class. We also see that though there is a vast difference between people who are self employed or not, the number of people who seek treatment in both the categories is more or less similar. Thus, we may conclude that whether a person is self employed or not, does not largely affect whether he may be seeking mental treatment or not.

This is the respondents answer to the question, 'Do you have a family history of mental illness?'.

From close to 40% of the respondents who say that they have a family history of mental illness, the plot shows that they significantly want to get treatment rather than without a family history. This is acceptable, remember the fact that people with a family history pay more attention to mental illness. Family history is a significant risk factor for many mental health disorders. Thus, this is an important factor that has to be taken under consideration as it influences the behaviour of the employees to a significant extent.

This was the respondent's answer to the question, 'If you have a mental health condition, do you feel that it interferes with your work?'.

This was the respondent's answer to the question, 'Do you work remotely (outside of an office) at least 50% of the time?'.

Around 70% of respondents don't work remotely, which means the biggest factor of mental health disorder came up triggered on the workplace. On the other side, it has slightly different between an employee that want to get treatment and don't want to get a treatment. The number of people who seek treatment in both the categories is more or less similar and it does not affect our target variable. Let's move forward with our next variable which is 'tech_company'.

This is the respondents answer to the question, 'Is your employer primarily a tech company/organization?'.

This was the respondent's answer to the question, 'Does your employer provide mental health benefits?'.

This was the respondent's answer to the question, 'Do you know the options for mental health care your employer provides?'. Since this graph is more or less similar to the benefits one, we won't be discussing it in more detail. Moving forward, the next category is wellness program. Lets try understanding that!

This is the respondents answer to the question, 'Has your employer ever discussed mental health as part of an employee wellness program?'.

This is the respondent's answer to the question, 'Is your anonymity protected if you choose to take advantage of mental health or substance abuse treatment resources?'.

This is the respondent's answer to the question, 'How easy is it for you to take medical leave for a mental health condition?'.

This is the respondent's answer to the question, 'Do you think that discussing a mental health issue with your employer would have negative consequences?'.

This is the respondent's answer to the question, 'Do you think that discussing a physical health issue with your employer would have negative consequences?'.

This is the respondent's answer to the question, 'Would you be willing to discuss a mental health issue with your coworkers?'.

This is the respondent's answer to the question, 'Would you be willing to discuss a mental health issue with your direct supervisor(s)?'.

This is the respondent's answer to the question, 'Do you think that discussing a mental health issue with your employer would have negative consequences?'.

This is the respondent's answer to the question, 'Would you bring up a physical health issue with a potential employer in an interview?'.

This was the respondent's answer to the question, 'Do you feel that your employer takes mental health as seriously as physical health?'.

This was the respondent's answer to the question, 'Have you heard of or observed negative consequences for coworkers with mental health conditions in your workplace?'.

Data Preparation

We have only two columns left that contain null values - work_interfere and self_employed. Let us try to fill these null values and make our data ready for further processing.

We can clearly see that all the columns except the 'Age', consist of object type values. We also notice that most of the columns consist of values 'Yes', 'No' , 'Maybe' etc. which can be easily encoded. So the next step that we would perform will be encoding.

We can see that the target column, i.e 'treatment' has almost equal values for both the categories. This means that we do not have to perform undersampling or oversampling. Now let us make a heatmap and try to understand the correlation of various features with the target variable.

Evaluating Model