SciModeler: A Toolbox for Consolidating Scientific Knowledge within the Field of Health Behavior Change

Science aims to build and advance general theories from empirical data. This process is complicated by the immense volume of empirical data and scientific theories in some domains, for example in the field of health behavior change. Especially, a systematic mapping between empirical data and theoret...

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Vydané v:SN computer science Ročník 4; číslo 1; s. 52
Hlavní autori: Nuijten, Raoul, Van Gorp, Pieter
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore Springer Nature Singapore 01.01.2023
Springer Nature B.V
Predmet:
Annotations
Automation
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Context
Data Structures and Information Theory
Empirical analysis
Empowerment
Graphs
Health behavior
Information Systems and Communication Service
Information technology
Intervention
Knowledge representation
Literature reviews
Mapping
Metamodels
Model-Driven Engineering and Software Development
Original Research
Pattern Recognition and Graphics
Resource Description Framework-RDF
Semantics
Software Engineering/Programming and Operating Systems
Solvents
Taxonomy
Telemedicine
Theoretical constructs
Vision
Theory-building
Metamodel
Health behavior change
ISSN:2661-8907, 2662-995X, 2661-8907
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Popis
Shrnutí:Science aims to build and advance general theories from empirical data. This process is complicated by the immense volume of empirical data and scientific theories in some domains, for example in the field of health behavior change. Especially, a systematic mapping between empirical data and theoretical constructs is lacking. We propose a toolbox to establish that mapping. We adopted a modeling approach based on literature surveys to elicit requirements and to derive a metamodel. We adopted a graph-based database system to implement the metamodel, and designed a web-based tool for importing data from annotated text documents. To evaluate that toolbox (named SciModeler ), we have conducted a case study within the field of health behavior change to record three scientific theories, three empirical studies, and the mapping in-between. We have documented how SciModeler  aids closing gaps between empirical data and theoretical constructs. We have demonstrated that this enables new types of analyses by sharing example queries for (1) refining scientific theories, (2) exploring promising intervention strategies for a specific context, and (3) checking the potential impact of an intervention platform in a specific context. Our supplementary materials promote replication of these results. SciModeler  can support the consolidation of scientific knowledge in the field of health behavior change, and we suggest that it may be applied within other fields, as well. An important direction for future work is promoting online collaboration on SciModeler  graphs.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-022-01444-y