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Grief Experience Patterns Among Older Adults in Rural China

A Latent Profile Analysis

Bibliographic Data

ID13627636
AuthorsHaimin Pan (0000-0003-3702-8552, Xiamen University, corresponding author), Rong Hu (0000-0002-4312-9248, Xiamen University)
Year2019
Volume83
Issue4
Pages898-913
Publication date2019-08-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueOMEGA - Journal of Death and Dying (JOURNAL)
Journal identifiersISSN: 0030-2228 • E-ISSN: 1541-3764
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/0030222819870408
PMID31455155
OpenAlexW2970516298
LanguageEN
Citations received3
References cited32

The present work sought to explore grief patterns among Chinese older people in rural areas as well as the factors influencing the diverse bereavement results. Participants were 352 older residents who lived in rural areas in China. Latent profile analysis was used to identify subtypes of class membership in combing complicated grief, depression, anxiety, and meaning in life. One-way analysis of variance, chi-square analysis, and multinomial regression analysis were performed together to examine the predictor best distinguishing between classes. The latent profile analysis model best fitting the data was a three-class solution comprised of adaptive ( n = 235; 66.8%), moderate maladaptive ( n = 83; 23.6%), and severe maladaptive groups ( n = 34; 9.7%). Compared to the severe maladaptive and moderate maladaptive groups, participants in the adaptive group had better physical functioning. Participants in the moderate maladaptive group had longer bereavement duration than those in the severe maladaptive group. Future replication is desirable for validating these subgroups

Anxiety · Complicated grief · Depression (economics · Grief · Latent class model · Psychiatry · Clinical Psychology · Grief, Bereavement, and Mental Health · Intergenerational Family Dynamics and Caregiving · Migration, Health and Trauma · Psychology

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Unique citing works3
Citations per year1,5
Citation span2024 - 2026 (3)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 3

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