Why Research From Lower- and Middle-Income Countries Matters to Evidence-Based Intervention: A State of the Science Review of ACT Research as an Example

Behav Ther. 2024 Nov;55(6):1348-1363. doi: 10.1016/j.beth.2024.06.003. Epub 2024 Jun 13.

Abstract

Despite the global nature of psychological issues, an overwhelming majority of research originates from a small segment of the world's population living in high-income countries (HICs). This disparity risks distorting our understanding of psychological phenomena by underrepresenting the cultural and contextual diversity of human experience. Research from lower- and middle-income countries (LMIC) is also less frequently cited, both because it is seemingly viewed as a "special case" and because it is less well known due to language differences and biases in indexing algorithms. Acknowledging and actively addressing this imbalance is crucial for a more inclusive, diverse, and effective science of evidence-based intervention. In this state-of-the-science review, we used a machine learning method to identify key topics in LMIC research on Acceptance and Commitment Therapy (ACT), choosing ACT due to the significant body of work from LMICs. We also examined one indication of study quality (study size), and overall citations. Research in LMICs was often nonindexed, leading to lower citations, but study size could not explain a lack of indexing. Many objectively identified topics in ACT research became invisible when LMIC research was ignored. Specific countries exhibited potentially important differences in the topics. We conclude that strong and affirmative actions are needed by scientific associations and others to ensure that research from LMICs is conducted, known, indexed, and used by CBT researchers and others interested in evidence-based intervention science.

Keywords: acceptance and commitment therapy; inequity; journal indexing; lower- and middle-income countries; topic modeling.

Publication types

  • Review

MeSH terms

  • Developing Countries*
  • Evidence-Based Practice
  • Humans
  • Machine Learning
  • Research