Volume 26, Issue 1 (4-2026)
Social Welfare Quarterly 2026, 26(1): 221-254 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Farzin Yazdi M, Setodenia F. (2026). Conceptual Structure Analysis and Research Gap Identification in the Field of Older Adults’ Mental Health: A Scientometric Approach. Social Welfare Quarterly. 26(1), 221-254. doi:10.32598/refahj.26.100.10
URL: http://refahj.uswr.ac.ir/article-1-4573-en.html
URL: http://refahj.uswr.ac.ir/article-1-4573-en.html
Full-Text [PDF 1100 kb]
(366 Downloads)
| Abstract (HTML) (1956 Views)
Full-Text: (31 Views)
Extended Abstract
Introduction
Given the growing elderly population and the complexities associated with their mental health and social well-being, a precise understanding of trends and research priorities is essential for social planning. This study aimed to analyze the conceptual structure, elucidate temporal developments, and identify existing research gaps in studies on the mental health of older adults, thereby outlining future directions for policy-making and the enhancement of social well-being.
Methods
This study employed a scientometric approach, integrating text mining and network analysis, to map the conceptual structure and thematic evolution of scientific literature on elderly mental health. Adopting a descriptive-analytical design, the study population comprised records retrieved from the Web of Science database. A comprehensive search strategy combining terms for “mental/psychological health” and “aging” initially identified 20,269 English-language documents (including articles, reviews, and conference papers). To ensure scientific rigor and mitigate linguistic bias, only English records were retained. Given the extensive dataset, automated preprocessing based on semantic relevance and keyword frequency was utilized to filter noise, bypassing manual screening.
Data processing, including keyword extraction, normalization, and synonym resolution, was conducted using Python libraries (Pandas and NLTK). Topic modeling via Latent Dirichlet Allocation (LDA) was applied to abstracts to identify major thematic clusters. The optimal number of topics was determined using the Coherence Score, which peaked at six topics (score: 0.65), representing the core research domains. Semantic differentiation and similarity among these topics were assessed using Jensen-Shannon Divergence (JSD).
To analyze thematic dynamics, the temporal distribution of topics was examined to identify emerging, stable, and declining trends. The Pruned Exact Linear Time (PELT) algorithm was employed for change-point detection in time series, assuming a normal distribution and minimum squared error cost, to pinpoint significant shifts in research output. Finally, a keyword co-occurrence network was constructed, where nodes represented keywords and edges denoted co-occurrence frequency. Weighted degree centrality was used to interpret network clusters. All analytical procedures were implemented in Python using NetworkX, Pandas, and Matplotlib.
Findings
Thematic Structure and Conceptual Clusters
Latent Dirichlet Allocation (LDA) modeling identified six distinct thematic clusters that structure the field of elderly mental health research. These clusters reflect the interdisciplinary nature of the field and a gradual paradigm shift from purely biomedical perspectives toward holistic, community-based approaches. The specific characteristics of each theme are as follows:
Clinical and Disease-Oriented Research (Biomedical Approach)
This cluster focuses on the intersection of mental health and chronic physical conditions. Key terms such as “patients,” “cancer,” “treatment,” and “risk” highlight a biomedical context where mental health is viewed as dependent on somatic diseases and clinical outcomes. Its primary distinction lies in viewing mental health as a consequence of physical illness within a clinical setting.
Psychological Well-being, Anxiety, and Mental Health Literacy (Individual Psychological Approach)
Centered on internal, individual factors, this theme addresses dimensions such as anxiety, mental well-being, and self-perception. Keywords like “anxiety,” “mental health,” and “health literacy” indicate a focus on cognitive and emotional factors influencing mental health. Unlike Topic 3, which focuses on external social factors, this theme concentrates on the individual’s internal psychological state and awareness.
Social Factors and Quality of Life (Socio-Environmental Approach)
This theme emphasizes social determinants, including quality of life, social support, and cultural contexts. Frequent terms such as “social,” “life,” “care,” and “elderly” reflect a conceptualization of mental health as a product of interaction with the environment and society. It contrasts with Topic 2 by focusing on external, socio-environmental influences rather than individual psychology.
Cognitive Functions and Age-Related Changes (Assessment and Diagnostic Approach): This cluster is dedicated to cognitive assessment and diagnosis. Keywords such as “memory,” “cognitive,” “performance,” and “testing” point to an emphasis on evaluating age-related cognitive decline. Its distinctiveness lies in its focus on measurement tools and diagnostic procedures, whereas other themes prioritize treatment, social determinants, or psychological states.
Evidence-Based Interventions (Methodological/Evaluative Approach): Reflecting the methodological maturity of the field, this theme focuses on the efficacy of psychological services. Terms like “interventions,” “systematic review,” and “evidence” indicate a strong orientation toward evaluating treatment outcomes through rigorous methodological frameworks.
Community-Based Mental Health, Dementia, and Emerging Public Health Challenges (Systemic/Community Approach): This cluster addresses mental health at the systemic level, including neurodegenerative diseases like “dementia” and the impact of public health crises (e.g., “COVID-19”). Unlike Topic 1’s focus on individual clinical treatment, this theme emphasizes crisis management, social care, and public health strategies at the community level.
The high frequency of core terms like “older adults” and “dementia” across all topics confirms that the research focus remains predominantly on the elderly population. The semantic similarity and differentiation among these six topics were quantified using Jensen-Shannon Divergence, which allowed for weighted analysis of topic distributions based on their significance.
Temporal Evolution and Conceptual Network
Temporal trend analysis revealed a significant shift in research focus over time. The field has gradually moved from purely treatment-oriented, biomedical approaches toward more holistic, preventive, and social perspectives. Recent trends highlight growing interest in quality of life, social isolation, and digital health, underscoring the need for social policies that enhance the well-being of older adults.
Keyword co-occurrence network analysis further delineated the conceptual structure, revealing five distinct clusters. The concepts of “quality of life” (frequency: 2,501), “depression” (1,597), and “dementia” (1,503) exhibited the highest centrality, identifying them as the pivotal nodes in the research landscape.
International Collaboration and Research Gaps
Analysis of the international collaboration network indicated that China plays a leading role in knowledge production in this field, with a degree centrality of 0.158. Despite the observed paradigm shift toward social and preventive approaches, significant research gaps persist. Specifically, there is a lack of sufficient research on “operational social interventions” and the “direct linkage of mental health with welfare policies.” These findings suggest that future research should bridge the gap between theoretical social perspectives and practical, policy-driven interventions to better address the complex needs of the aging population.
Discussion
The paradigm shift in mental health research for older adults from individual and clinical approaches to social and welfare contexts highlights the need to revise policies and design comprehensive social interventions to enhance the mental health and quality of life of this population. However, the identification of research gaps in operational social interventions serves as a serious warning for policymakers; it indicates that while theoretical literature has moved toward social well-being, operational tools and evidence-based interventions in this area remain insufficient. Furthermore, the dominance of leading countries such as China in knowledge production underscores the necessity of localizing research and generating indigenous knowledge tailored to the cultural and social needs of Iranian society. Ultimately, filling these research gaps by designing studies that directly link mental health with welfare programs appears essential for improving the well-being of older adults.
Ethical Considerations
Compliance With Ethical Guidelines: This research was conducted in accordance with ethical principles for human studies
Authors’ contributions: All authors have voluntarily and knowingly contributed to the preparation of this manuscript.
Funding: This study received no financial support from any organization or funding agency.
Conflict of Interest: The authors declare no conflict of interest regarding the publication of this article.
Introduction
Given the growing elderly population and the complexities associated with their mental health and social well-being, a precise understanding of trends and research priorities is essential for social planning. This study aimed to analyze the conceptual structure, elucidate temporal developments, and identify existing research gaps in studies on the mental health of older adults, thereby outlining future directions for policy-making and the enhancement of social well-being.
Methods
This study employed a scientometric approach, integrating text mining and network analysis, to map the conceptual structure and thematic evolution of scientific literature on elderly mental health. Adopting a descriptive-analytical design, the study population comprised records retrieved from the Web of Science database. A comprehensive search strategy combining terms for “mental/psychological health” and “aging” initially identified 20,269 English-language documents (including articles, reviews, and conference papers). To ensure scientific rigor and mitigate linguistic bias, only English records were retained. Given the extensive dataset, automated preprocessing based on semantic relevance and keyword frequency was utilized to filter noise, bypassing manual screening.
Data processing, including keyword extraction, normalization, and synonym resolution, was conducted using Python libraries (Pandas and NLTK). Topic modeling via Latent Dirichlet Allocation (LDA) was applied to abstracts to identify major thematic clusters. The optimal number of topics was determined using the Coherence Score, which peaked at six topics (score: 0.65), representing the core research domains. Semantic differentiation and similarity among these topics were assessed using Jensen-Shannon Divergence (JSD).
To analyze thematic dynamics, the temporal distribution of topics was examined to identify emerging, stable, and declining trends. The Pruned Exact Linear Time (PELT) algorithm was employed for change-point detection in time series, assuming a normal distribution and minimum squared error cost, to pinpoint significant shifts in research output. Finally, a keyword co-occurrence network was constructed, where nodes represented keywords and edges denoted co-occurrence frequency. Weighted degree centrality was used to interpret network clusters. All analytical procedures were implemented in Python using NetworkX, Pandas, and Matplotlib.
Findings
Thematic Structure and Conceptual Clusters
Latent Dirichlet Allocation (LDA) modeling identified six distinct thematic clusters that structure the field of elderly mental health research. These clusters reflect the interdisciplinary nature of the field and a gradual paradigm shift from purely biomedical perspectives toward holistic, community-based approaches. The specific characteristics of each theme are as follows:
Clinical and Disease-Oriented Research (Biomedical Approach)
This cluster focuses on the intersection of mental health and chronic physical conditions. Key terms such as “patients,” “cancer,” “treatment,” and “risk” highlight a biomedical context where mental health is viewed as dependent on somatic diseases and clinical outcomes. Its primary distinction lies in viewing mental health as a consequence of physical illness within a clinical setting.
Psychological Well-being, Anxiety, and Mental Health Literacy (Individual Psychological Approach)
Centered on internal, individual factors, this theme addresses dimensions such as anxiety, mental well-being, and self-perception. Keywords like “anxiety,” “mental health,” and “health literacy” indicate a focus on cognitive and emotional factors influencing mental health. Unlike Topic 3, which focuses on external social factors, this theme concentrates on the individual’s internal psychological state and awareness.
Social Factors and Quality of Life (Socio-Environmental Approach)
This theme emphasizes social determinants, including quality of life, social support, and cultural contexts. Frequent terms such as “social,” “life,” “care,” and “elderly” reflect a conceptualization of mental health as a product of interaction with the environment and society. It contrasts with Topic 2 by focusing on external, socio-environmental influences rather than individual psychology.
Cognitive Functions and Age-Related Changes (Assessment and Diagnostic Approach): This cluster is dedicated to cognitive assessment and diagnosis. Keywords such as “memory,” “cognitive,” “performance,” and “testing” point to an emphasis on evaluating age-related cognitive decline. Its distinctiveness lies in its focus on measurement tools and diagnostic procedures, whereas other themes prioritize treatment, social determinants, or psychological states.
Evidence-Based Interventions (Methodological/Evaluative Approach): Reflecting the methodological maturity of the field, this theme focuses on the efficacy of psychological services. Terms like “interventions,” “systematic review,” and “evidence” indicate a strong orientation toward evaluating treatment outcomes through rigorous methodological frameworks.
Community-Based Mental Health, Dementia, and Emerging Public Health Challenges (Systemic/Community Approach): This cluster addresses mental health at the systemic level, including neurodegenerative diseases like “dementia” and the impact of public health crises (e.g., “COVID-19”). Unlike Topic 1’s focus on individual clinical treatment, this theme emphasizes crisis management, social care, and public health strategies at the community level.
The high frequency of core terms like “older adults” and “dementia” across all topics confirms that the research focus remains predominantly on the elderly population. The semantic similarity and differentiation among these six topics were quantified using Jensen-Shannon Divergence, which allowed for weighted analysis of topic distributions based on their significance.
Temporal Evolution and Conceptual Network
Temporal trend analysis revealed a significant shift in research focus over time. The field has gradually moved from purely treatment-oriented, biomedical approaches toward more holistic, preventive, and social perspectives. Recent trends highlight growing interest in quality of life, social isolation, and digital health, underscoring the need for social policies that enhance the well-being of older adults.
Keyword co-occurrence network analysis further delineated the conceptual structure, revealing five distinct clusters. The concepts of “quality of life” (frequency: 2,501), “depression” (1,597), and “dementia” (1,503) exhibited the highest centrality, identifying them as the pivotal nodes in the research landscape.
International Collaboration and Research Gaps
Analysis of the international collaboration network indicated that China plays a leading role in knowledge production in this field, with a degree centrality of 0.158. Despite the observed paradigm shift toward social and preventive approaches, significant research gaps persist. Specifically, there is a lack of sufficient research on “operational social interventions” and the “direct linkage of mental health with welfare policies.” These findings suggest that future research should bridge the gap between theoretical social perspectives and practical, policy-driven interventions to better address the complex needs of the aging population.
Discussion
The paradigm shift in mental health research for older adults from individual and clinical approaches to social and welfare contexts highlights the need to revise policies and design comprehensive social interventions to enhance the mental health and quality of life of this population. However, the identification of research gaps in operational social interventions serves as a serious warning for policymakers; it indicates that while theoretical literature has moved toward social well-being, operational tools and evidence-based interventions in this area remain insufficient. Furthermore, the dominance of leading countries such as China in knowledge production underscores the necessity of localizing research and generating indigenous knowledge tailored to the cultural and social needs of Iranian society. Ultimately, filling these research gaps by designing studies that directly link mental health with welfare programs appears essential for improving the well-being of older adults.
Ethical Considerations
Compliance With Ethical Guidelines: This research was conducted in accordance with ethical principles for human studies
Authors’ contributions: All authors have voluntarily and knowingly contributed to the preparation of this manuscript.
Funding: This study received no financial support from any organization or funding agency.
Conflict of Interest: The authors declare no conflict of interest regarding the publication of this article.
Type of Study: orginal |
Received: 2026/02/10 | Accepted: 2026/05/5 | Published: 2026/05/31
Received: 2026/02/10 | Accepted: 2026/05/5 | Published: 2026/05/31
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Contact Information
Copyright © The Author(s);
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 (CC-By-NC 4.0), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Contact Information
