Welcome back, everyone! I'm Ayumi Furusawa, your host, and I'm truly grateful to have you join me for another episode as we delve into the complexities of special education. This season, we're exploring the intricate challenges surrounding the identification of children with disabilities for special education services—issues like disproportionate identification, misidentification, delayed identification, and, in some cases, the lack of identification altogether.

In our previous episode, we unpacked how systemic barriers—historical inequities, racism, ableism, the pathology model of disability, cultural stigma, and implicit biases—intertwine to create these challenges. By adopting a systems thinking approach (Brown & Smith, 2021), we can see how these factors are interconnected, shaping the experiences of children in profound ways.

Today, I invite you to journey with me as we explore the subtle yet powerful impact of implicit biases (Maina et al., 2018). Imagine a child stepping into a doctor's office or a classroom. Their future isn't determined solely by their needs or behaviors but is significantly influenced by the unconscious biases of the professionals responsible for identifying their eligibility for special education. These biases, often hidden beneath the surface, play a crucial role in shaping when and how children are diagnosed (Pham et al., 2022; Lewis & Solis, 2022).

We'll begin our exploration in the realm of healthcare. There are two primary pathways for diagnosing disabilities in children: medical diagnosis and educational diagnosis. Concerns about a child's development often first arise in medical settings, such as during visits to pediatricians or other healthcare providers (Angell et al., 2018). These professionals conduct developmental screenings and can initiate referrals for early intervention services. Understanding how implicit biases operate in healthcare is crucial because it sets the stage for the educational evaluations that follow. Later in this episode, we'll transition to the educational setting to see how these biases continue to influence the identification process in schools. By examining both settings, we'll provide a comprehensive roadmap of how implicit biases affect children throughout their developmental journey.

Unveiling Implicit Bias in Healthcare

In 2022, researcher Pham and her colleagues conducted a groundbreaking study to investigate how implicit biases among healthcare professionals contribute to racial and ethnic disparities in autism diagnoses. Picture the vast landscape of healthcare settings across the United States—from large urban hospitals bustling with activity to small pediatric clinics serving tight-knit communities, and private practices in suburban neighborhoods. Pham's team sought to capture how different types of healthcare providers handle autism diagnoses for children from diverse racial and ethnic backgrounds.

What sets this study apart is its sheer scale and depth (Pham et al., 2022). They analyzed data from over 1.4 million children's Electronic Health Records—imagine that, about half the population of Nevada. These records weren't just numbers and dates; they contained detailed information on each child's age at diagnosis, race and ethnicity, and rich clinical notes from healthcare providers. This treasure trove of data allowed the researchers to uncover patterns and nuances that smaller studies might have missed.

At the heart of their investigation was a critical question: How do implicit biases in healthcare providers affect both the timing and likelihood of receiving an autism diagnosis for Black, Latino, and White children?

To delve into this, the study used the Implicit Association Test, a widely recognized tool that measures unconscious biases by analyzing how quickly participants associate positive or negative words with specific social groups (Kurdi et al., 2019). Healthcare providers from each participating clinic, hospital, and practice took this test, revealing their implicit biases toward different racial and ethnic groups. By linking these bias scores to diagnostic outcomes in the Electronic Health Records, the researchers could see how a provider's unconscious attitudes might influence their clinical decisions.

Revealing the Impact

The results were both illuminating and concerning.

On average, Black children were diagnosed with autism six months later than their White peers, and Latino children experienced a 3.6-month delay (Pham et al., 2022). In the fast-paced world of early childhood development, even a few months can make a significant difference. Early intervention services are crucial for children with autism, and delays can mean missed opportunities for support that can enhance their communication, social skills, and overall development (Zerbo et al., 2018).

But it wasn't just about timing. The study also found that Black children were 1.8 times less likely to receive an autism diagnosis, and Latino children were 1.4 times less likely, compared to White children (Pham et al., 2022). This means that many children of color might not receive a diagnosis at all, depriving them of essential resources and interventions.

Understanding the Why

So, why were these disparities occurring?

The data revealed a clear connection between higher bias scores on the Implicit Association Test and the delays or absence of autism diagnoses for children of color (Pham et al., 2022). Healthcare providers with higher levels of implicit bias were more likely to overlook or misinterpret symptoms of autism in Black and Latino children. For instance, a provider might attribute a child's lack of eye contact or delayed speech to cultural differences or environmental factors rather than considering autism.

This unconscious filtering can have profound effects. Even in settings where decisions are expected to be based purely on medical evidence, these hidden biases can influence critical diagnostic decisions, leading to unequal care (Maina et al., 2018).

Reflecting on the Implications

The implications of Pham's (2022) study are significant. It highlights that healthcare decisions are not always as objective as we might assume. Implicit biases can act as invisible barriers, preventing children from receiving timely and accurate diagnoses.

For families, this means navigating a healthcare system that may not fully recognize or address their child's needs. It underscores the importance of increasing awareness about implicit biases and implementing training and policies to mitigate their impact.

Transitioning to Education

Now, let's shift our focus from the doctor's office to the classroom, another crucial environment where implicit biases can shape a child's educational journey.

Exploring Implicit Bias in Education

In the same year, 2022, Lewis and Solis conducted a comprehensive study examining how implicit biases contribute to disproportionality in special education placements, focusing on Black and Latino students. Imagine classrooms across the country—from urban schools with diverse student bodies to suburban schools with different demographics. Lewis and Solis sought to capture a broad picture of how various school environments handle special education referrals.

Exploring the Study

Their research involved 500 general education teachers and 200 special education professionals working in a range of schools—from low-income, high-minority districts to more affluent, majority-White districts (Lewis & Solis, 2022). By including such diverse settings, the study aimed to understand how implicit biases might manifest differently depending on the school's racial and socioeconomic makeup.

Educators completed the Implicit Association Test to assess their unconscious attitudes toward different racial and ethnic groups (Kurdi et al., 2019). They were also presented with hypothetical scenarios depicting common classroom challenges—like a student fidgeting during a lesson, expressing frustration when struggling with a task, or showing defiance when given instructions. These scenarios were designed to see if educators responded differently based on the student's perceived race or ethnicity.

Key Findings

The findings were striking.

Black students were 2.7 times more likely than their White peers to be identified with emotional or behavioral disorders (Lewis & Solis, 2022). Educators often misinterpreted behaviors like fidgeting or expressing frustration as defiance or aggression when exhibited by Black students. For example, a Black student who taps their pencil during class might be seen as intentionally disruptive, leading to referrals to behavior management programs that focus more on discipline than on addressing potential learning needs.

In contrast, Latino students were 1.9 times less likely to be identified with learning disabilities compared to their White peers (Lewis & Solis, 2022). Educators frequently attributed Latino students' academic struggles to language barriers or cultural differences rather than recognizing signs of a potential learning disability. As a result, these students might not receive the specialized support they need to succeed academically.

Implications for Students

This misinterpretation of behaviors based on race or ethnicity has profound consequences. For Black students, being disproportionately identified with behavioral disorders can lead to stigmatization and reduced access to rigorous academic content. For Latino students, under-identification of learning disabilities means missed opportunities for interventions that could support their educational growth (Sullivan & Bal, 2018).

Lewis and Solis (2022) emphasized that implicit biases deeply affect how student behaviors are interpreted, shaping their educational experiences and limiting their access to necessary resources. They called for systemic changes in how educators are trained to recognize and respond to student behavior, highlighting the need for greater awareness and strategies to counteract implicit biases.

Intersecting Biases and Systemic Ableism

Our final study today comes from Adams and Clark, who explored how systemic ableism—the institutionalized discrimination against people with disabilities—intersects with implicit biases in the U.S. education system. Their 2022 study examined how students of color with disabilities are identified—or, more often, misidentified or overlooked—for special education services.

Delving into the Research

Adams and Clark's (2022) study involved 1,000 students from diverse racial and ethnic backgrounds across various school settings, including urban and suburban public schools. They collected data from school records and teacher assessments and conducted interviews with educators to understand how implicit biases and ableism shaped the identification process.

They discovered that students of color with disabilities were 30% less likely to be correctly identified for special education services compared to their White peers (Adams & Clark, 2022). In many cases, educators overlooked or misinterpreted signs of disabilities in these students. For instance, behaviors indicating a disability—like difficulty concentrating or restlessness—were often viewed through a lens of racial bias, leading to misidentification.

Even more alarming, students of color with disabilities were 2.4 times more likely to be labeled as having behavioral issues such as defiance or aggression rather than being identified as needing disability support (Adams & Clark, 2022). For example, a Black student exhibiting frustration or restlessness might be seen as acting out rather than showing signs of an underlying condition like ADHD or a learning disability.

Consequences and Reflections

This misidentification often results in students being placed into behavior management programs that focus on discipline rather than providing the academic or therapeutic support they need (Sullivan & Proctor, 2016). It underscores how systemic ableism and racial biases are deeply embedded within educational structures, going beyond individual decisions to influence policies and practices.

Adams and Clark (2022) emphasized that addressing these disparities requires institutional change. They advocated for retraining educators to recognize the signs of disabilities in students of color and creating policies that ensure equity in special education identification and services.

Connecting the Dots

Across these three studies—by Pham and colleagues (2022), Lewis and Solis (2022), and Adams and Clark (2022)—a concerning pattern emerges: implicit biases significantly influence how children are diagnosed, identified for services, and supported, both in healthcare and education.

In healthcare, implicit biases among professionals lead to delays or missed diagnoses for children of color, particularly regarding autism. This underdiagnosis creates barriers to accessing early intervention services, which are crucial for developmental progress and long-term outcomes (Zerbo et al., 2018).

In education, implicit biases affect how student behaviors are interpreted and who gets referred for special education. Black students are overrepresented in categories like emotional or behavioral disorders due to misinterpretations of their behaviors, while Latino students are underrepresented in learning disability categories because their struggles are often attributed to language or cultural differences.

When considering systemic ableism, these biases compound, leading to students of color with disabilities being misidentified or overlooked entirely. This double layer of bias—against both their race and their disability—hinders their access to appropriate support and resources.

Moving Forward

Understanding these biases is the first crucial step toward addressing the inequities in how children are identified and supported in special education. By increasing awareness and providing training for healthcare providers and educators to recognize and mitigate their implicit biases, we can begin to dismantle these unseen barriers (Harris et al., 2020). Revising policies and practices to ensure equitable identification processes and involving families and communities in conversations about these issues fosters trust and collaboration. Together, these efforts can create a more just system where every child has the opportunity to receive the support they need.

Closing Thoughts

Thank you for joining me on this exploration of how implicit biases affect the identification of children with disabilities in healthcare and education. These unseen forces shape children's futures in profound ways, and it's essential that we bring them to light.

As we continue this important conversation, stay tuned for our next episode, where we'll focus on special education policy—specifically the Individuals with Disabilities Education Act (IDEA). We'll delve into how IDEA impacts the identification process and discuss the broader policy challenges that contribute to ongoing issues in special education.

I'm Ayumi Furusawa, your host, and I look forward to having you with us again next time. In the meantime, please visit my website at shiftingparadigm.org—that's S-H-I-F-T-I-N-G P-A-R-A-D-I-G-M dot org—for show notes, references, and additional resources. Let's continue to explore these critical challenges associated with identification for special education.

Thank you for being part of this journey, and I look forward to our next discussion. See you soon!

1. Culturally Responsive Practices: 

Approaches in education and healthcare that respect and integrate the cultural backgrounds of students and patients to reduce bias and ensure fair treatment (Adams & Clark, 2022)

2. Delayed Identification: 

When a child’s disability is diagnosed later than it should be, often because of biases that cause healthcare providers or educators to overlook early signs (Pham et al., 2022)

3. Disproportionate Identification: 

When certain racial or ethnic groups are either overrepresented or underrepresented in special education due to biases (Lewis & Solis, 2022)

4. Implicit Bias: 

Unconscious attitudes or stereotypes that affect how people make decisions and interact with others, often without realizing it (Maina et al., 2018; Lewis & Solis, 2022)

5. Misidentification: 

Incorrectly labeling a child with the wrong disability, often due to biases, which can lead to inappropriate education services (Adams & Clark, 2022)

6. Stigma: 

Negative attitudes and beliefs that devalue, exclude, or discriminate against people based on certain conditions or behaviors (Lewis & Solis, 2022; Adams & Clark, 2022)

7. Systemic Ableism: 

Discrimination against people with disabilities that is built into societal structures, including schools, which can lower expectations and limit opportunities for students with disabilities (Adams & Clark, 2022)

Adams, M. S., & Clark, T. H. (2022). Systemic ableism and implicit biases in the American educational system: Impacts on disability identification and support. Disability Studies Quarterly, 42(4).

Angell, A. M., Empey, A., & Zuckerman, K. E. (2018). A review of diagnosis and service disparities among children with autism from racial and ethnic minority groups in the United States. International Review of Research in Developmental Disabilities, 55, 145-180.

Brown, T., & Smith, J. (2021). Applying systems thinking to special education identification processes. Journal of Educational Change, 22(3), 345-360.

Harris, B., Reyes, N., & Hill, T. (2020). Clinical and school identification and intervention for youth with ASD: Culturally and linguistically responsive interdisciplinary considerations. In Care Coordination for Pediatric Autism Spectrum Disorder (pp. 91-108). Springer.

Kurdi, B., Seitchik, A. E., Axt, J. R., Carroll, T. J., Karapetyan, A., Kaushik, N., ... & Banaji, M. R. (2019). Relationship between the Implicit Association Test and intergroup behavior: A meta-analysis. American Psychologist, 74(5), 569-586.

Lewis, A. E., & Solis, J. L. (2022). Implicit bias and special education identification: Addressing disproportionality through training and policy reform. Journal of Special Education Leadership, 35(2), 167-179.

Maina, I. W., Belton, T. D., Ginzberg, S., Singh, A., & Johnson, T. J. (2018). A decade of studying implicit racial/ethnic bias in healthcare providers using the implicit association test. Social Science & Medicine, 199, 219-229.

Pham, H. H., Sandberg, N., Trinkl, J., & Thayer, J. (2022). Racial and ethnic differences in rates and age of diagnosis of autism spectrum disorder. JAMA Network Open, 5(10), e2239604.

Sullivan, A. L., & Bal, A. (2018). Disproportionality in special education: Effects of individual and school variables on disability risk. Exceptional Children, 84(1), 54-75.

Sullivan, A. L., & Proctor, S. L. (2016). The shifting landscape of educational risk: Risk and resilience in the context of a changing society. In Handbook of Multicultural School Psychology (pp. 53-73). Routledge.

Zerbo, O., Massolo, M. L., Qian, Y., & Croen, L. A. (2018). A study of physician knowledge and experience with autism in adults in a large integrated healthcare system. Journal of Autism and Developmental Disorders, 49(6), 2696-2707.

Host: Ayumi Furusawa

Episode Overview:

In this episode, we explored the challenges that implicit biases present in identifying children with disabilities, particularly focusing on healthcare and education. Below is a recap of the key points discussed.

How Bias Affects Diagnosing Autism in Healthcare

We discussed how implicit biases can delay the diagnosis of autism in children of color. Pham et al. (2022) studied over 1.4 million children's medical records and found that Black children were diagnosed with autism six months later than White children, and Hispanic children faced a 3.6-month delay. These delays prevent children from accessing early intervention services that can make a dramatic difference in their development. This shows how healthcare providers' unconscious biases play a role in who gets diagnosed and when.

Bias in Schools: Misidentifying Kids for Special Education

Implicit biases also affect how children of color are identified for special education in schools. Lewis and Solis (2022) found that African American students are 2.7 times more likely than White students to be labeled with emotional or behavioral disorders. This means normal behaviors may be seen as problematic because of biases. In contrast, Latino students are 1.9 times less likely to be identified as having learning disabilities, suggesting that their challenges may be misunderstood or overlooked.

Adams and Clark (2022) explored how racial bias and ableism together lead to unfair treatment of students of color with disabilities. They found that students of color with disabilities are 30% less likely to get the special education services they need and are more often misidentified as having behavior problems. This can prevent them from receiving the right support.

Why It Matters: The Bigger Picture

All these studies show a pattern: implicit biases in both healthcare and education lead to delays, misdiagnoses, and misidentifications for children of color with disabilities. In healthcare, these biases result in delayed autism diagnoses, which means children miss out on critical early intervention (Pham et al., 2022). In schools, biases cause African American students to be overrepresented in emotional and behavioral disorder categories, while Latino students are often under-identified for learning disabilities (Lewis & Solis, 2022). Moreover, systemic ableism, as Adams and Clark (2022) noted, makes things even harder for these students, often leading them to be placed in inappropriate educational programs. These findings make it clear that biases have long-term effects on children’s futures, influencing their access to the services they need to thrive.

Resources:

• Visit shiftingparadigm.org for show notes, references, and additional resources.