Commentary: If not you, then who: Equity, social justice, and the role of school psychologists in ensuring reading success for all learners.
Nicole Patton TerryKizzy AlbrittonPublished in: School psychology (Washington, D.C.) (2022)
Over the last 2 years, the world has witnessed an unprecedented set of events that have changed the course of history. The global COVID-19 pandemic heightened awareness of both social, economic, and health disparities and racial injustices suffered by Black, Brown, and poor people in the United States. Although we are still learning about the impact of these disruptions on learning and achievement, emerging data indicates that children's experiences varied widely and that disparities that were present before the pandemic only got worse. Thus, it is not surprising that, this time around, conversations about reading have pivoted towards attempts to include an equity and social justice perspective. We argue that a focus on specific evidence-based practices or interventions to address differences in reading achievement among subgroups of students may be insufficient. Instead, a more comprehensive approach that focuses on achieving equitable systems (e.g., addressing the historical, political, social, or economic systems which produce and contribute to disparities) and equitable processes (e.g., implementing organizational structures and adopting specific practices that broaden participation and elevate perspectives from marginalized groups) may be required for meaningful and sustainable change. In this commentary, we offer three ways school psychologists can advance reading research and practice towards more equitable reading success. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Keyphrases
- mental health
- healthcare
- working memory
- physical activity
- primary care
- mental illness
- public health
- young adults
- coronavirus disease
- high school
- affordable care act
- high resolution
- quality improvement
- climate change
- risk assessment
- machine learning
- african american
- deep learning
- human health
- adverse drug
- mass spectrometry
- health information