Evidence map›Paper›PMID 41398251›Full record

ArticleBMC palliative care2025

Choice, struggle, and resilience: the experience of pre-loss grief among Chinese family caregivers of cancer patients.

Sheng Bao, Yubing Chen

Abstract read
In one paragraph

Article in BMC palliative care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Sheng BaoSchool of Journalism and Communication, Jinan University, 601 Huangpu Avenue West, Guangzhou, 510632, China.
Yubing ChenSchool of Journalism and Communication, Jinan University, 601 Huangpu Avenue West, Guangzhou, 510632, China. robinchen.cyb@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPre-loss grief (PLG) is a critical challenge for cancer caregivers. In China, where family-centered care is profoundly shaped by cultural scripts like filial piety, understanding the digital expression of this experience is crucial for developing effective support. This study aimed to investigate the digitally expressed experience of PLG among Chinese caregivers.

methodsA computationally assisted qualitative analysis was performed on 63,775 user-generated texts from the social media platform Xiaohongshu. We used topic modeling and keyword co-occurrence network analysis to identify initial semantic patterns, which were then developed into final themes through a reflexive thematic analysis guided by a constructivist epistemology.

resultsFour core themes were identified: (1) Deep Involvement in Medical Decision-Making; (2) Caregiving Burden and Psycho-Physical Suffering; (3) Adaptive Shift in Family Communication Strategies; and (4) Narratives of Resilience and Meaning-Making. These themes reveal that the PLG experience is a dynamic process of navigating multiple tensions, including cultural responsibilities, personal suffering, relational adjustments, and seeking meaning.

conclusionsThe PLG experience of Chinese caregivers is a dynamic process defined by profound tensions. These findings underscore grief's culturally constructed nature, highlighting the need for proactive and culturally sensitive support systems that acknowledge both the inherent burdens and the potential for growth during caregiving.

Indexed as

CaregiversChoice BehaviorGriefNeoplasmsResilience, PsychologicalAdaptation, PsychologicalAdultAgedChinaEast Asian PeopleFemaleHumansMaleMiddle AgedQualitative ResearchCancer caregiversPre-loss griefSocial mediaTopic modeling

Identifiers

PMID41398251
PMCPMC12822318

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.