Introduction

In the fast-paced, high-pressure world of the tech industry, mental health challenges are both common and deeply complex. Long hours, intense deadlines, and the always-on digital environment have contributed to rising rates of anxiety, burnout, and depression among tech professionals. Yet despite the increasing visibility of mental health conversations, many workers still face significant barriers to seeking support — whether due to stigma, unsupportive work environments, or lack of access to resources.

This project looks at mental health in the tech world using data from a survey conducted by Open Sourcing Mental Illness (OSMI). The goal is to explore how different factors — like age, gender, company culture, and personal background — influence whether someone feels comfortable talking about mental health, or decides to seek treatment. I wanted to better understand how workplace environments either support or discourage people from getting help, and how that varies across different types of workers.

Visualization 1: How Common is Mental Health Treatement in Tech?

Before diving into the factors that influence mental health support in the tech workplace, it’s important to understand the overall landscape. One of the most telling indicators from the dataset is the high percentage of respondents who report being diagnosed with a mental health condition at some point in their lives.

The chart below shows the breakdown of responses to the question: “Have you ever been diagnosed with a mental health condition by a medical professional?”

This simple comparison lays the foundation for the rest of the project. The majority of participants report having received a diagnosis, which suggests that mental health struggles are not only present in tech — they are widespread. Given how common these experiences are, the next natural questions are: who seeks help, who doesn’t, and why?

So I created this pie chart visualization that shows there’s an almost even spread among survey participants who have received mental health treatments, where about 50.6% of them responded with “yes”, and 49.4% of them answered “no”.

Visualization 2: Treatment & Family History

Family history of mental illness can shape a person’s awareness and openness toward mental health care. For some, having a close relative with mental health struggles may reduce the stigma or hesitation around seeking support. Others may recognize early signs in themselves because they’ve seen them in family members. In this section, we explore how respondents with and without a family history of mental illness differ in whether they’ve sought professional treatment. The goal is to better understand whether personal exposure to mental health conditions makes a difference in one’s own willingness to seek help.

In this bar graph I created, there is a clear relationship between family history and treatment-seeking behavior. Among respondents with a family history of mental illness, nearly three-quarters (74.2%) have sought professional treatment. In contrast, only 35.5% of those without a family history reported doing the same. This suggests that personal exposure to mental health issues within one’s family may increase awareness, reduce stigma, or encourage early intervention when facing similar challenges.

Visualization 3: Work Interference & Treatment

Mental health doesn’t just affect personal well-being — it often spills into professional life. One key way to understand this impact is by looking at whether individuals feel their mental health interferes with their ability to work. Some might experience mild disruptions, while others may feel overwhelmed or unable to perform. In this section, we examine how self-reported levels of work interference relate to whether someone has sought mental health treatment. Do those who feel more affected by their mental health at work tend to seek help, or is treatment equally likely regardless of workplace disruption?

This interactive donut chart explores how individuals’ experiences with mental health–related work interference correlate with whether they have sought mental health treatment. The two rings represent different groups:

  • The outer ring includes respondents who have received treatment

  • The inner ring represents those who have not received treatment

Each ring is segmented by self-reported levels of interference: “Often,” “Sometimes,” “Rarely,” and “Never.” Among those who have sought treatment, the majority (56.6%) report “Sometimes” experiencing interference, followed by “Often” (19.4%) and “Rarely” (19.3%). Only 4.7% report “Never” being affected. In contrast, among those who have not sought treatment, over half (50.6%) say they “Never” experience interference, with the rest more evenly distributed across “Sometimes” (29.6%), “Rarely” (14.1%), and “Often” (5.8%). Individuals who feel their mental health frequently or occasionally interferes with work are more likely to seek treatment. Meanwhile, those reporting minimal or no disruption tend not to pursue professional help. This suggests a strong link between perceived workplace impact and the likelihood of seeking support.

Visualization 4: Workplace Support & Mental Health Treatment

The decision to seek mental health treatment can be shaped not only by personal experiences, but also by institutional support. In workplace environments, factors like access to benefits, clear care options, and protection of anonymity may reduce barriers and make individuals feel more comfortable asking for help. This interactive Shiny app allows users to explore how these organizational policies relate to the likelihood of employees seeking mental health treatment. By selecting different aspects of support, we can examine which workplace practices are most strongly associated with treatment-seeking behavior.

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This interactive Shiny app examines how different forms of workplace support—such as mental health benefits, care options, and anonymity—relate to employees’ decisions to seek mental health treatment. The bar chart visualizes the proportion of respondents who have or have not sought treatment (y-axis), grouped by their responses to the selected workplace support variable (x-axis). For instance, if “Benefits” is selected, the x-axis displays categories like “Yes,” “No,” and “Don’t know,” indicating whether respondents reported access to mental health benefits at their workplace. Each bar is stacked to show the treatment rate within that group. Users can apply filters for gender, remote work status, and company size to narrow the analysis to specific subpopulations. This allows for a flexible comparison of treatment behavior across different workplace environments and demographic groups. For example, selecting only female respondents working remotely in mid-sized companies reveals whether support availability influences their likelihood of seeking treatment. This visualization enables a deeper understanding of how organizational resources and personal context shape mental health decisions in professional settings.

Visualization 6: Perceptions of Employer Support and Treatment-Seeking Behavior Across Age Groups

Employees’ willingness to seek mental health treatment often hinges not only on personal need but also on how they perceive their employer’s attitude toward mental health. If mental health is treated with the same seriousness as physical health in the workplace, it can foster a more supportive culture that encourages help-seeking behavior. In this section, we investigate how these employer perceptions influence treatment rates across different age groups and genders. By incorporating an animated comparison, we aim to visualize how mental health stigma in workplace culture might play a role in shaping behavior across demographics.

The animated scatter plot illustrates the proportion of individuals in each age group who have sought mental health treatment, segmented by gender and animated by whether the employer is perceived to treat mental health as seriously as physical health. The y-axis represents the percentage of people who reported receiving treatment, while the x-axis breaks this down by age group. Gender is distinguished by color.

In workplaces without strong perceived support (“No”), younger age groups—particularly males—are less likely to seek treatment. For example, in the 18–24 range, males report the lowest treatment rates compared to other genders. As we transition to the “Yes” frame, where employers are seen as equally supportive of mental and physical health, the treatment rates visibly increase across age and gender, especially among younger cohorts. This suggests a strong link between perceived organizational support and individuals’ likelihood to pursue care—underscoring the importance of destigmatizing mental health from the top down

Visualization 7: Global Mental Health Treatment Rates

As conversations surrounding mental health become more global, it’s important to recognize that treatment-seeking behavior varies significantly across countries and cultures. This final visualization aims to examine the geographic distribution of mental health treatment rates around the world and explore how these rates differ by gender, age, and remote work status. By providing interactive filters, this Shiny app allows users to investigate how demographic patterns intersect with geographic context to shape the likelihood of seeking treatment. Understanding these global variations can help inform region-specific mental health initiatives and workplace support strategies.

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The map displays average mental health treatment rates by country, with each point sized according to sample size and shaded by treatment proportion—darker blue indicating higher rates. In the default view, countries like New Zealand and various Northern and Western European nations stand out with relatively high treatment proportions, while several others across Asia, Eastern Europe, and Latin America display lighter shades, indicating lower treatment rates. Users can filter the data by gender, age group, and remote work status to observe how specific subpopulations are represented globally. For instance, selecting only females aged 25–34 who work remotely may reveal distinct geographic clusters compared to older, non-remote males. This tool highlights the diverse international landscape of mental health care engagement.

Visualization 8: Mental Health vs. Physical Health

In the final section, I’ll shift focus to the perceived consequences employees anticipate when disclosing health issues to their employers. Specifically, I examine whether employees expect negative outcomes from sharing mental health conditions versus physical ones. Understanding these perceptions is essential, as fear of repercussions can create barriers to help-seeking and open communication in the workplace. By comparing responses across the two domains, I aim to reveal potential stigma unique to mental health that may not be present for physical health concerns.

The slope chart illustrates how perceptions differ when it comes to disclosing mental versus physical health issues at work. Each line represents a category of perceived consequence (“yes”, “maybe”, or “no”) and shows how its share changes between the two health contexts. Notably, the proportion of respondents who believe there would be no negative consequences increases substantially when the issue is physical (green line). Meanwhile, those who answered “yes” (expecting negative consequences) decreases from mental to physical health, and the “maybe” group also declines. These trends suggest that mental health disclosures are more likely to be perceived as risky or uncertain, reinforcing the stigma that still surrounds mental health in professional settings.

Conclusion

Throughout this project, I explored how people around the world experience mental health in the workplace — from whether they seek treatment to how they perceive employer support. Using a mix of static and interactive visualizations, I looked at how treatment patterns vary by age, gender, country, and work conditions like remote work and company size. I built Shiny apps that let users dig into these differences in real time, and used animations and 3D plots to bring key trends to life. Along the way, I found that workplace culture and perceived support really matter — they often shape whether someone feels safe enough to get help. These findings highlight the importance of open conversations and supportive policies when it comes to mental health at work.