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Content Characteristics of Knowledge Integration in the eHealth Field: An Analysis Based on Citation Contexts

Shiyun Wang

Shiyun Wang

  • Center for Studies of Information Resources, Wuhan UniversityWuhan, China
  • School of Information Management, Wuhan UniversityWuhan, China
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Wang, Shiyun
, 
Jin Mao

Jin Mao

  • Center for Studies of Information Resources, Wuhan UniversityWuhan, China
  • School of Information Management, Wuhan UniversityWuhan, China
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Mao, Jin
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Jing Tang

Jing Tang

  • Center for Studies of Information Resources, Wuhan UniversityWuhan, China
  • School of Information Management, Wuhan UniversityWuhan, China
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Tang, Jing
 e 
Yujie Cao

Yujie Cao

School of Information Management, Central China Normal UniversityWuhan, China
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Cao, Yujie
  
02 mar 2021
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Categoria dell'articolo: Research Paper
Pubblicato online: 02 mar 2021
Pagine: 58 - 74
Ricevuto: 01 nov 2020
Accettato: 05 feb 2021
DOI: https://doi.org/10.2478/jdis-2021-0015
Parole chiave
© 2021 Shiyun Wang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

The knowledge amount distribution for each knowledge type from 1999 to 2018. The panel on the left (a) shows the total number of AKPs for each knowledge type over the period, and the inside subgraph in (a) presents the number of eHealth papers in our dataset between 1999 and 2018. The panel on the right (b) shows the proportion of knowledge amount of each knowledge type in each year.
The knowledge amount distribution for each knowledge type from 1999 to 2018. The panel on the left (a) shows the total number of AKPs for each knowledge type over the period, and the inside subgraph in (a) presents the number of eHealth papers in our dataset between 1999 and 2018. The panel on the right (b) shows the proportion of knowledge amount of each knowledge type in each year.

Figure 2

The number of references with the AKPs. (a), The total number of references with the AKPs for each knowledge type from 1999 to 2018. (b), The proportion of references with the corresponding type of AKPs in each year. The ratio of references for each knowledge type in every year was calculated by the references with the corresponding type of knowledge divided by the total number of references with AKPs in that year. Notably, one reference may contain different types of knowledge.
The number of references with the AKPs. (a), The total number of references with the AKPs for each knowledge type from 1999 to 2018. (b), The proportion of references with the corresponding type of AKPs in each year. The ratio of references for each knowledge type in every year was calculated by the references with the corresponding type of knowledge divided by the total number of references with AKPs in that year. Notably, one reference may contain different types of knowledge.

Figure 3

The number of source disciplines of the AKPs. (a), The total number of distinct source disciplines with AKPs between 1999 and 2018. (b), The proportion of distinct source disciplines with AKPs for each knowledge type in each year. The ratio of disciplines for each knowledge type in every year was calculated by the distinct disciplines containing the corresponding type of knowledge divided by the total number of distinct disciplines with AKPs in that year. Notably, one distinct discipline may contain different types of knowledge.
The number of source disciplines of the AKPs. (a), The total number of distinct source disciplines with AKPs between 1999 and 2018. (b), The proportion of distinct source disciplines with AKPs for each knowledge type in each year. The ratio of disciplines for each knowledge type in every year was calculated by the distinct disciplines containing the corresponding type of knowledge divided by the total number of distinct disciplines with AKPs in that year. Notably, one distinct discipline may contain different types of knowledge.

Figure 4

The average citation interval of AKPs for each knowledge type.
The average citation interval of AKPs for each knowledge type.

Figure 5

The co-occurrence frequency of knowledge types within citation context and its ratio to the sum of the two knowledge types. The heatmap was drawn based on the ratio value.
The co-occurrence frequency of knowledge types within citation context and its ratio to the sum of the two knowledge types. The heatmap was drawn based on the ratio value.

Top 10 source disciplines for each knowledge type_

Research Subject Entity Technology Research Methodology Data Theory
Health Care Sciences & Services Health Care Sciences & Services Health Care Sciences & Services Health Care Sciences & Services Health Care Sciences & Services Public, Environmental & Occupational Health
Medical Informatics Medical Informatics Medical Informatics Medical Informatics Medical Informatics Health Care Sciences & Services
Public, Environmental & Occupational Public, Environmental & Occupational Public, Environmental & Occupational Public, Environmental & Occupational Public, Environmental & Occupational Medical Informatics
Health Medicine, General & Internal Health Medicine, General & Internal Health Medicine, General & Internal Health Psychiatry Health Medicine, General & Internal Psychology, Multidisciplinary
Psychiatry Psychiatry Computer Science, Information Systems Medicine, General & Internal Information Science & Library Science Management
Psychology, Clinical Nursing Information Science & Library Science Psychology, Clinical Computer Science, Information Systems Psychology, Applied
Substance Abuse Psychology, Clinical Computer Science, Interdisciplinary Application Substance Abuse Computer Science, Interdisciplinary Application Psychology, Social
Health Policy & Services Health Policy & Services Psychiatry Health Policy & Services Health Policy & Services Psychology
Nursing Substance Abuse Psychology, Clinical Psychology Multidisciplinary Sciences Psychology, Clinical
Endocrinology & Metabolism Computer Science, Information Systems Substance Abuse Psychology, Multidisciplinary Psychiatry Computer Science, Information Systems

The knowledge classification schema for AKPs_

Category Description Literature sources
Research Subject subject terms related to research problems, such as diseases and research areas. Heffernan & Teufel, 2018; Kondo et al., 2009
Theory theory related phrases, e.g., specific names of theories, and frameworks Wang & Zhang, 2018; Pettigrew & McKechnie, 2001
Research Methodology research methodology, including research methods, scales, guidelines, evaluation indicators, etc. Sahragard & Meihami, 2016; Heffernan & Teufel, 2018; Mesbah et al., 2017; Radoulov, 2008;
Technology Entity techniques, devices, and systems people or organizations that are involved in any aspect of the research Gupta & Manning, 2011; Tsai et al., 2013 Bahadoran et al., 2019
Data phrases related to datasets, data sources, and data material Wang & Zhang, 2018; Sahragard & Meihami, 2016; Mesbah et al., 2017; Radoulov, 2008
Others other phrases that are not included in the above categories, e.g., geolocations, projects, etc. Kondo et al., 2009

Annotation example of each knowledge category_

AKPs Citation sentences Knowledge type
chronic illness For effective medical care of chronic illness, such as Type 2 diabetes mellitus (T2DM), adequate and sustainable self-management initiated by patients is important Research Subject
social cognitive theory The intervention, including both the SMS text messaging and individual counseling session, was modeled after national treatment guidelines, and guided by Social Cognitive Theory and the stages of change model Theory
qualitative research methodology In recent years, qualitative research methodology has become more recognized and valued in diabetes behavioral research because it helps answer questions that quantative research might not, by exploring patient motivations, perceptions, and expectations Research Methodology
SMS text messaging Consistent with the literature, SMS text messaging was an appropriate and accepted tool to deliver health promotion content Technology
heart failure patient De Vries et al (2013) evaluated the actual use and goals of telemonitoring systems, whereas Seto et al (2012) developed a randomized trial of mobile phone-based telemonitoring systems to examine the experience of heart failure patients with these systems Entity
bacteriology datum PDA-based technologies were used to develop a PDA-based electronic system to collect, verify, and upload bacteriology data into an electronic medical record system; develop a wireless clinical care management system; and develop a data collection/entry system for public surveillance data collection Data
low risk Free et al found that while mHealth studies have been conducted many are of poor quality, few have a low risk of bias, and very few have found clinically significant benefits of the interventions Others

Integration characteristics of different knowledge types_

Knowledge type Knowledge amount Distinct AKPs References Source disciplines Knowledge integration density Average citation interval
Research Subject 104,988 15,324 51,622 187 2.03 5.91
Entity 25,213 1,665 18,219 150 1.38 5.33
Technology 17,945 1,885 13,256 157 1.35 4.22
Research Methodology 9,099 2,079 6,773 144 1.34 7.74
Data 3,297 296 2,822 124 1.17 5.11
Theory 1,315 225 921 88 1.43 10.55
Others 84,310 4,290 44,346 190 1.90 5.50

Brief information of our dataset_

Statistical items Value
Citing papers 3,221
Citation sentences 119,598
References 101,751
In-text citations 199,461
AKPs 246,167
Distinct AKPs 25,764
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Informatica, Tecnologia informatica, Project Management, Base dati e data mining
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