Evidence map›Paper›PMID 33999833›Full record

ArticleJournal of medical Internet research2021

COVID-19 Knowledge Resource Categorization and Tracking: Conceptual Framework Study.

Muhammad Afzal, Maqbool Hussain, Jamil Hussain, Jaehun Bang, Sungyoung Lee

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

5 authors.

Muhammad AfzalDepartment of Software, Sejong University, 209 Neungdong-ro, Seoul, Republic of Korea.ORCID 0000-0002-7851-2327
Maqbool HussainDepartment of Software, Sejong University, 209 Neungdong-ro, Seoul, Republic of Korea.ORCID 0000-0002-1663-1718
Jamil HussainDepartment of Data Science, Sejong University, Seoul, Republic of Korea.ORCID 0000-0003-3862-8787
Jaehun BangDepartment of Computer Science and Engineering, Kyung Hee University, Yongin, Republic of Korea.ORCID 0000-0003-3675-2258
Sungyoung LeeDepartment of Computer Science and Engineering, Kyung Hee University, Yongin, Republic of Korea.ORCID 0000-0002-5962-1587

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince the declaration of COVID-19 as a global pandemic by the World Health Organization, the disease has gained momentum with every passing day. Various private and government sectors of different countries allocated funding for research in multiple capacities. A significant portion of efforts has been devoted to information technology and service infrastructure development, including research on developing intelligent models and techniques for alerts, monitoring, early diagnosis, prevention, and other relevant services. As a result, many information resources have been created globally and are available for use. However, a defined structure to organize these resources into categories based on the nature and origin of the data is lacking.

objectiveThis study aims to organize COVID-19 information resources into a well-defined structure to facilitate the easy identification of a resource, tracking information workflows, and to provide a guide for a contextual dashboard design and development.

methodsA sequence of action research was performed that involved a review of COVID-19 efforts and initiatives on a global scale during the year 2020. Data were collected according to the defined structure of primary, secondary, and tertiary categories. Various techniques for descriptive statistical analysis were employed to gain insights into the data to help develop a conceptual framework to organize resources and track interactions between different resources.

resultsInvestigating diverse information at the primary, secondary, and tertiary levels enabled us to develop a conceptual framework for COVID-19-related efforts and initiatives. The framework of resource categorization provides a gateway to access global initiatives with enriched metadata, and assists users in tracking the workflow of tertiary, secondary, and primary resources with relationships between various fragments of information. The results demonstrated mapping initiatives at the tertiary level to secondary level and then to the primary level to reach firsthand data, research, and trials.

conclusionsAdopting the proposed three-level structure allows for a consistent organization and management of existing COVID-19 knowledge resources and provides a roadmap for classifying future resources. This study is one of the earliest studies to introduce an infrastructure for locating and placing the right information at the right place. By implementing the proposed framework according to the stated guidelines, this study allows for the development of applications such as interactive dashboards to facilitate the contextual identification and tracking of interdependent COVID-19 knowledge resources.

Indexed as

Consumer Health InformationCOVID-19Health ResourcesHumansKnowledgePandemicsSARS-CoV-2COVID-19dashboardsdependency trackingdigital healthinformation organizationinformation technologyinteractive dashboardknowledge graphspandemicresource managementtracing information

Identifiers

PMID33999833
PMCPMC8171286

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.