What do we do in Gerontechnology? Mapping the field through semantic consensus analysis (2017-2025).
Purpose: This study presents a systematic analysis of research trends in papers published in the journal Gerontechnology from 2017 to 2025 using semantic consensus classification with multiple large language models. Methods: A total of 183 papers were analyzed based on their titles and abstracts. Th...
| Publicado en: | Gerontechnology Vol. 25; no. 1; pp. 1 - 12 |
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| Autores principales: | , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
International Society for Gerontechnology
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194473113&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194473113 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15691101 904R jtl: Gerontechnology issn: 15691101 maglogo: N pubinfo: dt: 2026 vid: 25 iid: 1 pid: 54298 pub: International Society for Gerontechnology artinfo: ui: 194473113 194473113 194473113 10.4017/gt.2026.25.1.1257.03 194473113 ppf: 1 ppct: 11 formats: tig: atl: What do we do in Gerontechnology? Mapping the field through semantic consensus analysis (2017-2025). aug: au: Hsu, Yeh-Liang Bhekumuzi, Mathunjwa Yang, Zong-Huan affil: Department of Mechanical Engineering, Yuan Ze University, Taiwan sug: subj: Consensus Research, Medical Trends Semantics Natural Language Processing Geriatrics Bibliometrics Human Aging Assistive Technology Dementia Digital Health Serial Publications ab: Purpose: This study presents a systematic analysis of research trends in papers published in the journal Gerontechnology from 2017 to 2025 using semantic consensus classification with multiple large language models. Methods: A total of 183 papers were analyzed based on their titles and abstracts. Three large language models participated in an iterative semantic consensus classification process to assign papers to five technical solution categories and one general category. Papers in technical solution categories were further classified using the same semantic consensus classification process according to the Gerontechnology Matrix, which consists of five application domains and four main goals. Download statistics and author-provided keywords were analyzed to examine readership patterns and thematic evolution. Results: General Issues constitutes the largest category, while the technical solution categories are represented in broadly comparable proportions. Matrix mapping shows that most technical solution papers are concentrated in the Health application domain, particularly associated with the goal of Prevention & Engagement, while other domains such as Housing, Communication, and Work & Leisure reflect distinct functional roles. Keyword analysis reveals sustained focus on aging, dementia, and assistive technologies, alongside increasing attention to accessibility, care contexts, interaction platforms such as smartphones and digital voice assistants, and emerging AI-based systems. The large number of unique keywords highlights substantial thematic diversity across published research. Conclusion: AI-assisted semantic consensus classification using multiple large language models, combined with structured keyword analysis, provides a scalable, reproducible approach for examining research trends in journal publications and for ongoing monitoring of developments in gerontechnology. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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