Evidence map›Paper›PMID 41368511›Full record

ArticleJournal of hospice and palliative care2025

A Text Network Analysis of Research on the Bereaved After Sudden Death Since 2000.

Kyung-Ah Kang, Suk-Jung Han, Jiyoung Chun

Abstract read
In one paragraph

Article in Journal of hospice and palliative care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Kyung-Ah KangLogos health Design institute, College of Nursing, Sahmyook University, Seoul, Korea.ORCID https://orcid.org/0000-0002-3799-9554
Suk-Jung HanLogos health Design institute, College of Nursing, Sahmyook University, Seoul, Korea.ORCID https://orcid.org/0000-0002-8824-2199
Jiyoung ChunLogos health Design institute, College of Nursing, Sahmyook University, Seoul, Korea.ORCID https://orcid.org/0000-0001-8604-5581

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to analyze research trends in those left behind after sudden bereavement in 2000, using text network analysis. Methods: This study structured and examined keywords in the abstracts of articles extracted from a web-based database. Five major databases (PubMed, Embase, Cochrane, CINAHL, and PsycInfo) were searched for key terms associated with bereavement and grief. After the data extraction, three dictionaries (thesaurus, exclusion, and definition) were created to refine the terms. A co-occurrence matrix was constructed, and text network analysis was performed to identify and visualize the core research topics. Results: In total, 108 studies on those left behind after bereavement were published, with <9 papers released annually. The top five words extracted from the studies on individuals left behind after bereavement were family, relationship, child, parent, and support. Cohesion analysis confirmed that the community could be divided into five categories (support by need, diseases and symptoms, changes due to bereavement, causes and processes of bereavement, and damage to the family) based on studies of individuals left behind after bereavement. Conclusion: Research addressing those left behind after sudden death is scarce, and only 108 studies were identified. These five clusters suggest that the research topics related to sudden bereavement represent distinct grief experiences characterized by unique psychological, social, and contextual features.

Indexed as

BereavementSocial network analysisSudden death

Identifiers

PMID41368511
PMCPMC12683177

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.