March 19, 2026
Nearly half of the 60 million U.S. adults living with a mental illness received no treatment in the past year — not because they couldn’t access care, but because they didn’t recognize they needed it. For HR leaders, benefits administrators, and higher education professionals, this is the core challenge: the employees and students you serve may be struggling without the language to name what they’re experiencing. Mental health literacy — the ability to recognize, understand, and act on mental health symptoms — is the missing infrastructure between distress and care.
According to the National Survey on Drug Use and Health (SAMHSA, 2024), only 3.4% of untreated adults with mental illness reported that they had tried and failed to access treatment. The primary reason for not seeking help—cited by 4 out of 5 untreated adults with mental illness—was that they did not perceive a need for it.
Mental health literacy (MHL) is defined as “knowledge and beliefs about mental disorders which aid their recognition, management, or prevention” (Jorm, 2021). Seeking help from a mental health professional is not a frictionless singular event but rather a long process of cognitive transitions.
Research demonstrates that the ability to correctly label a problem is a functional prerequisite for help-seeking. If an individual cannot name their experience as “depression,” they are less likely to turn to a mental health professional for treatment. Instead, they may seek out physical solutions for somatic symptoms — back pain, fatigue, or insomnia — leading to a cycle of clinical stalls in primary care where the underlying psychological drivers are never addressed.
This recognition deficit is often compounded by alexithymia — the inability to identify and describe emotions in the self. For the alexithymic individual, the silent gap is not merely a lack of external resources, but a disconnection from internal data points. When a person cannot differentiate between physiological arousal and emotional distress, they cannot formulate the help-seeking intentions necessary to bridge the gap to care. They are not ignoring their mental health per se; rather, they are fundamentally unable to read the signals that would alert them to a psychiatric need.
Without the terminology to label their internal experiences, individuals can remain trapped in a state of prodromal silence, misattributing clinical symptoms to things like stress or character flaws. This state can last for over a decade, with an 11-year average delay between the onset of mental health symptoms and the first clinical encounter (NAMI, 2024).
When lack of vocabulary is a barrier, a mental health screen can function like a dictionary. Digital mental health screenings — short, validated tools implemented by a software app — do not just provide quantitative psychopathology measures for clinicians and researchers. They also create teachable moments, especially for those struggling with alexithymic traits, to educate them about their mental and emotional states and available treatment options.
When users engage with anonymous digital screenings, they are presented with a list of symptoms that mirror their internal state. This symptom-mirroring serves two strategic goals:
Validation: For example, a digital screen can confirm that “irritability” or “lack of focus” is not a personal failure but a recognized clinical signpost.
Standardization: The results of a digital screen can provide users with a standardized language for communicating their distress to a medical provider.
Data from Mental Health America (MHA) underscores the efficacy of this screening-to-literacy pipeline. In 2023, MHA recorded over 6.5 million online screens. Crucially, 79% of those who scored in the moderate-to-severe range had never received a diagnosis. For these individuals, the screening was the very first time their distress was labeled with clinical precision.
Research conducted across the globe — including university cohorts in the United States, Australia, the Maldives, South Korea, the Netherlands, and Germany — consistently finds that individuals with higher mental health literacy have greater help-seeking intentions (Cheng et al., 2018; Clough et al., 2019; Hussain & Zaini, 2025; Kim, 2023; Reichel et al., 2023). When individuals gain the literacy to understand their symptoms, their help-seeking intentions increase (Kruzan et al., 2022; Smith & Shochet, 2011).
The theoretical mechanism at work here is one of cognitive load reduction (Baxter et al., 2025). Traditionally, patients must do all the heavy lifting: identify the problem, find a provider, and navigate the insurance labyrinth. By providing immediate screening and literacy, a proactive digital bridge reduces one of those burdens — problem identification. This fosters an objective, medicalized perspective that reduces the emotional reluctance associated with treatment, allowing individuals to view therapy not as a personal failure, but as a standard medical intervention.
If we can further provide a warm handoff, we can lift even more of that cognitive load. For example, at the point of explaining their screening results, we can provide a clickable link to start the appointment process. Research shows that basic assistance with things like knowing the geographical location of local healthcare services, understanding perceived availability, and estimating waiting times — directly impact the transition from a nebulous desire for relief into the concrete action of booking an appointment (Tomczyk et al., 2020).
To close the treatment gap for mental illness, we must equip community members with the tools to realize they need help. For digital health partners and community providers, the directive is clear: implement digital front doors that prioritize anonymous, validated screening. These tools can meet someone in the prodromal phase of illness before a crisis defines their first encounter. By engineering tools that increase MHL at scale, we can facilitate the cognitive shift that transforms vague feelings of distress into the concrete language that empowers action. However, we must acknowledge that, while MHL is a critical weapon in the public health arsenal, it is not a magic bullet. Its impact on help-seeking is moderated by several factors, including sociodemographic barriers related to age, gender, and cultural norms. In the next series of blog posts, we will take some deep dives into what are called high-need/low-utilization cohorts, the specific barriers they face, and what we can do to help.
Dr. Jason Shumake is the Director of Data Science and Clinical Research at Aiberry, specializing in computational psychiatry, precision psychological medicine, and digital mental health. He previously served as a Research Professor and Chief Data Scientist at the University of Texas at Austin’s Institute for Mental Health Research, where his work included partnering with UT’s Counseling and Mental Health Center. Across his academic and industry career, he has co-authored over 60 peer-reviewed publications, extensively evaluating treatments for depression and anxiety and using machine learning to predict the efficacy of internet-based behavioral interventions. Currently, Dr. Shumake is dedicated to transforming how we screen and engage people with untreated mental health challenges, leveraging technology to make mental health assessments more objective, accessible, and empowering for those taking their first step toward treatment
Baxter, K. A., Sachdeva, N., & Baker, S. (2025). The Application of Cognitive Load Theory to the Design of Health and Behavior Change Programs: Principles and Recommendations. Health Education & Behavior, 52(4), 469–477. https://doi.org/10.1177/10901981251327185
Cheng, H.-L., Wang, C., McDermott, R.C., Kridel, M. and Rislin, J.L. (2018). Self-stigma, mental health literacy, and attitudes toward seeking psychological help. Journal of Counseling & Development, 96: 64-74. https://doi.org/10.1002/jcad.12178
Clough, B. A., Nazareth, S. M., Day, J. J., & Casey, L. M. (2019). A comparison of mental health literacy, attitudes, and help-seeking intentions among domestic and international tertiary students. British Journal of Guidance & Counselling, 47(1), 123–135. https://doi.org/10.1080/03069885.2018.1459473
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Kruzan, K. P., Meyerhoff, J., Nguyen, T., Mohr, D. C., Reddy, M., & Kornfield, R. (2022). “I wanted to see how bad it was”: Online self-screening as a critical transition point among young adults with common mental health conditions. CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM.
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