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Cited by 25Psychology Research and BibliometricsMindfulness and Compassion InterventionsMeta-analysis and systematic reviews

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

Open access
Sahdra, Baljinder K., King, Grant, Payne, Jennifer S., Ruiz, Francisco J., Ali Kolahdouzan, Seyed, Ciarrochi, Joseph, Hayes, Steven C.·2024·Behavior Therapy
DOI 10.1016/j.beth.2024.06.003
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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.

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  • While these discussions have primarily centered on fostering cultural competence at the level of the care provider, there has been limited exploration of how each specific process of ACT should be adapted to distinct cultural contexts.

    “However, while these discussions have primarily centered on fostering cultural competence at the level of the care provider, there has been limited exploration of how each specific process of ACT should be adapted to distinct cultural contexts.33”

    From Differential associations of Acceptance and Commitment Therapy processes with psychological distress across racial groups. (2026) · cites this paper as [33]

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Authors
Sahdra, B. K., King, G., Payne, J. S., Ruiz, F. J., Ali Kolahdouzan, S., Ciarrochi, J., & Hayes, S. C.
Affiliations
  • Australian Catholic University (AU)
  • University of Michigan (US)
  • Johns Hopkins University (US)
  • Fundación Universitaria Konrad Lorenz (CO)
  • University of Nevada, Reno (US)
Corresponding author
Sahdra, Baljinder K.
ORCID
Venue
Behavior Therapy
Publisher
Elsevier BV
Published
2024-11
Volume
55
Issue
6
Pages
1348-1363
ISSN
0005-7894 (ISSN-L)
DOI
10.1016/j.beth.2024.06.003
OpenAlex
W4399721356
Cited by
25
References
42
Citations by year
2026: 11 · 2025: 12 · 2024: 2
FWCI
1.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Behavior Therapy (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion, public-domain)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
  • Social Sciences › Psychology › Clinical Psychology › Mindfulness and Compassion Interventions
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
acceptance and commitment therapy, evidence-based interventions, indexing algorithms, cultural diversity in psychology, study size, research visibility, cognitive behavioral therapy
MeSH terms
Machine Learning, Developing Countries, Humans, Research, Evidence-Based Practice

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