Academic Journal
Assigning Article-Level Themes in Bibliometric Analysis: Mode-Based Mapping Approach Using JMIR Aging Publications.
| Τίτλος: | Assigning Article-Level Themes in Bibliometric Analysis: Mode-Based Mapping Approach Using JMIR Aging Publications. |
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| Συγγραφείς: | Ho SY; Department of Emergency Medicine, Chi Mei Medical Center, No. 901, Chung Hwa Road, Yung Kung Dist, Tainan, Taiwan.; Center for Integrative Medicine, Chi Mei Medical Center, No. 901, Chung Hwa Road, Yung Kung Dist, Tainan, Taiwan., Tsai KT; Department of Family Medicine, Chi Mei Medical Center, 901 Chung Hwa Road, Yung Kung Dist, Tainan, 72263, Taiwan, 886 0937399106, 886 937399106.; Department of Family Medicine, Well Clinic, No. 302, Yunong Rd, East Dist, Tainan, 701011, Taiwan. |
| Πηγή: | JMIR aging [JMIR Aging] 2026 May 28; Vol. 9, pp. e79906. Date of Electronic Publication: 2026 May 28. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: JMIR Publications Inc Country of Publication: Canada NLM ID: 101740387 Publication Model: Electronic Cited Medium: Internet ISSN: 2561-7605 (Electronic) Linking ISSN: 25617605 NLM ISO Abbreviation: JMIR Aging Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [Toronto, ON] : JMIR Publications Inc., [2018]- |
| Ιατρικοί όροι (MeSH): | Bibliometrics* , Clustering Algorithms* , Models, Statistical*, Serial Publications/statistics & numerical data ; Geriatrics ; Humans |
| Περίληψη: | Background: Although clustering techniques are commonly used in bibliometric analysis to identify research themes, few studies systematically assign these themes back to individual articles. This gap limits the interpretability of findings and hinders granular, article-level longitudinal analysis. Objective: This study introduces the Theme Assignment Algorithm for Articles (TAAA), a data-driven framework designed to map clustered themes to individual publications. We demonstrate its utility by identifying dominant research patterns and thematic shifts within JMIR Aging. Methods: TAAA was applied to 434 JMIR Aging articles published between 2020 and 2025. Keywords were harvested from 3 sources: Web of Science Core Collection (WoSCC) Keywords Plus, author-provided keywords, and abstract-derived terms. These were grouped into thematic clusters using the "following leader clustering algorithm". The TAAA, implemented via R and a web-based application, determined each article's primary theme using a statistical model to create a discrete article-level variable. Core themes were identified via h-index computation. Analytical visualization included Kano and Sankey diagrams, alongside volcano plots and heatmaps. The framework's robustness was further tested by applying a "differentially expressed genes" analogy to map "unknown" core metadata across "known" pre/post publication stages using Cohen kappa as the extent of mapping power. Results: Analysis across the 3 keyword sources yielded 9, 7, and 9 core themes, respectively. The most prominent themes identified were HEALTH (39.4%), OLDER ADULTS (43.1%), and DEMENTIA (25.3%). Notably, DEMENTIA emerged as a consistent core theme across all sources and visual layers, validating the TAAA's ability to capture cross-source thematic coherence. The adaptation of dual heatmaps demonstrated the algorithm's capacity for comparative bibliometric mapping in JMIR Aging. A mapping precision of 0.33 provided quantitative evidence of a 2-stage publication pattern, though the separation between stages was not strictly confined to predefined time intervals. Conclusions: The TAAA framework provides a replicable, scalable, and interpretable method for article-level thematic assignment. Its ability to uncover consistent research patterns-specifically the dominance of dementia-related studies in JMIR Aging-demonstrates its value for bibliometricians and its potential adaptability to other domains, such as bioinformatics-inspired meta-analyses. (© Sam Yu-Chieh Ho, Kang-Ting Tsai. Originally published in JMIR Aging (https://aging.jmir.org).) |
| Contributed Indexing: | Keywords: FLCA clustering; R programming; bibliometric analysis; following leader clustering algorithm; theme assignment; visual analytics |
| Entry Date(s): | Date Created: 20260528 Date Completed: 20260701 Latest Revision: 20260701 |
| Update Code: | 20260701 |
| PubMed Central ID: | PMC13218566 |
| DOI: | 10.2196/79906 |
| PMID: | 42208041 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2561-7605 |
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| DOI: | 10.2196/79906 |