Week 1: An Introduction

Hi, my name is Aashirya and welcome to my Week 1 Senior Project Blog! In this blog I will be going over some background information and prior research that has been done, and I will be introducing my project and advisors as well as some things to look forward to in Week 2. 

Background Information:

According to the World Health Organization, the global prevalence of anxiety and depression increased by 25% during the first year of the pandemic, due to the stress, anxiety, and loneliness people experienced during isolation. Although mental health issues were brought to the forefront with the pandemic, people have been struggling with issues for much longer. Mental illness affects people of all ages and backgrounds, and according to the CDC, 1 in 5 American adults will experience a mental illness in a given year. 

One industry where mental health issues have been prevalent is the tech industry. According to the OSMI mental health in tech survey, 90% of tech workers report having been diagnosed with a mental health disorder and 64.7% of these workers said their productivity has been affected by this mental health issue. Currently, companies provide limited mental health resources, which are insufficient for employees to work through improving their conditions. This is why it is so important for tech companies to address their employees’ mental health before both the organizations and individuals are severely impacted.

My Project:

In this project, I will research current mental health treatment options and the level of support provided by companies, and I will survey employees, predominantly in the tech industry, regarding the status of their mental health and/or the support they may receive. 

Next, I will build three types of machine learning models which will try to predict whether or not an employee received mental health treatment. The goal of this project is to use the results of these models to assist companies in determining the amount of resources they need to provide their employees to improve their mental health.

Prior Research:

The idea of trying to predict whether someone will receive mental health treatment is not entirely new. Medical professionals currently look at biological, social, and psychological factors to determine signs of psychiatric disorders for early interventions. In a study done in 2021 by researchers at McGill University, 52 young people living in Quebec city, who had been followed since birth, had brain imaging scans that measured features of their dopamine reward pathway. These features were combined with info about their temperamental traits and histories of early life adversity in order to predict the subject’s mental health treatment status. The combination of these 3 factors predicted with 90% accuracy which participants had mental health problems either in the past or during the 3 year follow up period of this study. Due to the revolutionary findings of this study, the CIHR has provided an additional 2 million dollars to double the sample size and follow the participants through their mid-20s. 

As important as these studies are, they are expensive and time-consuming, in order to predict the participants’ mental health status, the participants had to be followed since birth. If I could create a model that predicts mental health treatment status with a high accuracy, that would be much more efficient and cheap to use.

Advisors And Looking Ahead To Next Week:

For this project, my internal advisor is Mrs. Bhattacharya, and my external advisors are Ali Karim and Sapna Saha. Ali is a Senior Architect in Data and Infrastructure at Stanford University, with experience in creating and training Machine Learning and Deep Learning models. Sapna is a clinical therapist certified in Cognitive Behavioral Therapy by the American Psychological Association. 

As for next week, my plan is to conduct research on current measures to promote employee mental health in the tech industry and evaluate their effectiveness. I will also continue to research current methods to predict whether someone may receive treatment for mental health and begin creating the survey that I will administer to various tech employees. 

Thank you for reading, and see you next week!

Sources:

• Technology Networks. “Three Factors Could Predict Mental Health With 90% Accuracy.” Neuroscience From Technology Networks, Technology Networks, 9 Dec. 2021, Www.Technologynetworks.Com/Neuroscience/News/Three-Factors-Could-Predict-Mental-Health-With-90-Accuracy-356656.
• World. “Mental Health.” Who.Int, World Health Organization: WHO, 17 June 2022, Www.Who.Int/News-Room/Fact-Sheets/Detail/Mental-Health-Strengthening-Our-Response.
• Ruiz, Ezequiel. “Supporting Mental Health In Tech Culture – BairesDev.” BairesDev, 16 Aug. 2022, Www.Bairesdev.Com/Blog/Supporting-Mental-Health-In-Tech-Culture/#:~:Text=More\%20than\%2090\%25\%20of\%20workers,By\%20a\%20mental\%20health\%20issue.
• Mental Health In The Workplace. 2023, Www.Cdc.Gov/Workplacehealthpromotion/Tools-Resources/Workplace-Health/Mental-Health/Index.Html.