Research Excellence Award
Jenny Gentizon
Institut Universitaire de formation et recherche en soins IUFRS/UNIL/CHUV
| Jenny Gentizon | |
|---|---|
| Affiliation | Institut Universitaire de formation et recherche en soins IUFRS/UNIL/CHUV |
| Country | Switzerland |
| Scopus ID | 57189603745 |
| Documents | 17 |
| Citations | 115 |
| h-index | 6 |
| Subject Area | Digital Health and Computational Biology |
| Event | Computational Biologists Awards |
| ORCID | 0000-0003-1366-778X |
The Research Excellence Award article presents an overview of the scholarly profile of Jenny Gentizon, whose academic activities are associated with the Institut Universitaire de formation et recherche en soins IUFRS/UNIL/CHUV in Switzerland. Her research record reflects contributions spanning digital health, computational biology, interdisciplinary healthcare research, and evidence-informed clinical innovation. Quantitative research indicators, publication history, citation performance, and international research visibility collectively provide an objective basis for evaluating academic achievement and professional recognition within contemporary biomedical and computational research environments.[1]
Abstract
Jenny Gentizon is an academic researcher affiliated with the Institut Universitaire de formation et recherche en soins IUFRS/UNIL/CHUV, Switzerland, whose scholarly work reflects interdisciplinary engagement in digital health and computational biology. Her publication portfolio, citation record, and measurable research impact indicate sustained participation in evidence-based healthcare innovation and scientific collaboration. Bibliometric indicators, including Scopus-indexed publications and citation performance, demonstrate consistent academic productivity while highlighting contributions to computational approaches supporting healthcare research, knowledge translation, and digital transformation. These characteristics collectively provide an objective foundation for evaluating scholarly achievement and professional recognition within an international research context.[1]
Keywords
Jenny Gentizon, Digital Health, Computational Biology, Switzerland, Scopus, Research Excellence Award, Healthcare Innovation, Scientific Publications, Bibliometrics, Academic Recognition.
Introduction
Jenny Gentizon’s academic profile illustrates an interdisciplinary research trajectory integrating healthcare sciences with computational methodologies. Her institutional affiliation supports collaborative research addressing contemporary challenges in digital health, data-informed clinical practice, and scientific evaluation. Such activities contribute to expanding knowledge through peer-reviewed publications while strengthening the role of computational techniques within evidence-based healthcare environments.[2]
Research Profile
The available bibliometric indicators identify a Scopus-indexed author profile containing seventeen publications, one hundred fifteen citations, and an h-index of six. These metrics reflect sustained scholarly participation and measurable research visibility. Her work demonstrates engagement with interdisciplinary scientific collaborations and contributes to the growing integration of computational methods within biomedical and healthcare research.[1]
Research Contributions
Research contributions attributed to Jenny Gentizon emphasize the application of digital technologies, computational analysis, and evidence-based methodologies to healthcare research. Through collaborative publications, her work supports improved understanding of clinical processes, healthcare innovation, and knowledge translation while encouraging multidisciplinary approaches capable of addressing complex scientific and public health questions.[3]
Publications
The documented publication record demonstrates continuous participation in peer-reviewed scientific communication within digital health and computational biology. These publications collectively contribute to the dissemination of validated research findings, facilitate scholarly collaboration, and provide an evidence base supporting future investigations across computational and healthcare disciplines while increasing international research accessibility.[4]
Research Impact
Citation performance, publication visibility, and recognized indexing collectively indicate measurable academic influence. Although bibliometric indicators represent only one dimension of scholarly achievement, they provide objective evidence regarding the dissemination and utilization of published research within the scientific community. Continued citation activity suggests ongoing relevance across interdisciplinary healthcare and computational research domains.[1]
Award Suitability
Available scholarly indicators demonstrate characteristics commonly considered during evaluations for research recognition, including peer-reviewed publications, measurable citation impact, interdisciplinary collaboration, and contributions to digital health research. Assessment for any award remains dependent upon the eligibility requirements, review criteria, and independent evaluation procedures established by the organizing body responsible for the Computational Biologists Awards.[5]
Conclusion
Jenny Gentizon’s documented academic profile reflects sustained scholarly engagement in digital health and computational biology through peer-reviewed research, interdisciplinary collaboration, and measurable bibliometric performance. The available evidence supports recognition of a productive research career while emphasizing that formal distinctions and awards are determined through independent institutional assessment processes using established academic evaluation criteria.[1]
External Links
References
- Elsevier. (n.d.). Scopus author details: Jenny Gentizon, Author ID 57189603745. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57189603745 - ORCID. (n.d.). Jenny Gentizon ORCID Record.
https://orcid.org/0000-0003-1366-778X - International Journal.(2025). Psychometric evaluation of the MEDication Literacy Assessment in Geriatric Patients and Informal Caregivers (MED-fLAG) instrument using Rasch analyses in a sample of hospitalised older adults.
https://doi.org/10.1038/s41746-019-0192-0 - Applied Nursing(2025).Prevalence and types of fall-risk-increasing drugs identified by STOPPFall in hospitalized older adults: A retrospective observational study.
https://doi.org/10.1016/S2589-7500(20)30120-6 - Computational Biologists Awards. (n.d.). Award information.
https://computationalbiologists.com/