Previously submitted to: JMIR Mental Health (no longer under consideration since Nov 15, 2025)
Date Submitted: Nov 13, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring themes and disparities in SI research across low-and-middle income countries using natural language processing
ABSTRACT
Background:
Despite bearing the largest burden of suicide globally, low- and middle-income countries (LMICs) remain significantly underrepresented in suicide research compared to high-income countries (HICs).
Objective:
This study leverages natural language processing (NLP) and qualitative analysis to examine disparities in suicide research between LMICs and HICs with a particular focus on suicidal ideation (SI). Additionally, the study aims to identify themes in LMIC SI research and explore trends within these themes over time.
Methods:
An analysis of 15,708 articles published between 1968 and 2022 was conducted, extracting country of focus using article titles and abstracts through NLP methods. Among the 1,458 articles focusing on SI in LMICs, Latent Dirichlet Allocation (LDA) and manual qualitative theming were used to identify and examine thematic clusters.
Results:
Results show that only 26.6% of SI articles focused on LMICs while 70.5% focused on HICs. Among the papers focusing on LMICS, five key themes emerged, including: developmental and lifespan context; interpersonal and risk factors; clinical instruments and care; gender, sex, perinatal and discrimination; and suicide attempts and death. Analyses were also used to identify trends in how often these themes were discussed within LMIC research over time.
Conclusions:
Findings highlight the need for increased SI research in LMICs in additional to improved research infrastructure and support. This study also demonstrates the utility of NLP and qualitative methodologies for large-scale research syntheses and provides promising directions for future global disparities research.
Citation
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Copyright
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