Evidence map›Paper›PMID 35103615›Full record

ArticleJournal of medical Internet research2022

Web-Based Research Trends on Child and Adolescent Cancer Survivors Over the Last 5 Years: Text Network Analysis and Topic Modeling Study.

Hyun-Yong Kim, Kyung-Ah Kang, Suk-Jung Han, Jiyoung Chun

Abstract read
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Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hyun-Yong Kim *Logos Health Design Institute, Sahmyook University, Seoul, Republic of Korea.ORCID 0000-0002-4356-3941
Kyung-Ah Kang *College of Nursing, Sahmyook University, Seoul, Republic of Korea.ORCID 0000-0002-3799-9554
Suk-Jung Han *College of Nursing, Sahmyook University, Seoul, Republic of Korea.ORCID 0000-0002-8824-2199
Jiyoung Chun *College of Nursing, Sahmyook University, Seoul, Republic of Korea.ORCID 0000-0001-8604-5581

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBeing diagnosed with cancer during childhood or adolescence can disrupt important periods in an individual's physical, psychosocial, and spiritual development and potentially reduce the quality of life (QOL) after treatment. Research is urgently required to improve the QOL for child and adolescent cancer survivors, and it is necessary to analyze the trends in prior research reported in international academic journals to identify knowledge structures.

objectiveThis study aims to identify the main keywords based on network centrality, subgroups (clusters) of keyword networks by using a cohesion analysis method, and the main theme of child and adolescent cancer survivor-related research abstracts through topic modeling. This study also aims to label the subgroups by comparing the results of the cohesion and topic modeling.

methodsA text network analysis method and topic modeling were used to explore the main trends in child and adolescent cancer survivor research by structuring a network of keyword (semantic morphemes) co-occurrence in the abstracts of articles published in 5 major web-based databases from 2016 to 2020. A total of 1677 child and adolescent cancer survivor-related studies were used for data analyses. Data selection, processing, and analyses were also conducted.

resultsThe top 5 keywords in terms of degree and eigenvector centrality were risk, control interval, radiation, childhood cancer treatment, and diagnosis. Of the 1677 studies used for data analyses, cluster 1 included 780 (46.51%) documents under risk management, cluster 2 contained 557 (33.21%) articles under health-related QOL and supportive care, and cluster 3 consisted of 340 (20.27%) studies under cancer treatment and complications.

conclusionsThis study is significant in that it confirms the knowledge structure based on the main keywords and cross-disciplinary trends in child and adolescent cancer survivor research published in the last 5 years worldwide. The primary goal of child and adolescent cancer survivor research is to prevent and manage the various aspects of the problems encountered during the transition to a normal life and to improve the overall QOL. To this end, it is necessary to further revitalize the study of the multidisciplinary team approach for the promotion of age-specific health behaviors and the development of intervention strategies with increased feasibility for child and adolescent cancer survivors.

Indexed as

Cancer SurvivorsNeoplasmsAdolescentChildHumansInternetMotivationQuality of LifeSurvivorsadolescentcancer survivorschildknowledge structuresresearch trendstext network analysistopic modeling

Identifiers

PMID35103615
PMCPMC8848247

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.