Evidence map›Paper›PMID 41048910›Full record

ArticleFrontiers in psychiatry2025

Patterns and predictors of post-traumatic growth and fear of disease progression in breast cancer patients: a latent profile analysis.

Keying Guo, Haipeng Li, Weina Du, Ling Cheng, Wei Wang, Zhongtao Zhou, Jing Zhang

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Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

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0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Keying Guo *College of Nursing, Bengbu Medical University, Bengbu, Anhui, China.
Haipeng Li *College of Mental Health, Bengbu Medical University, Bengbu, Anhui, China.
Weina Du *College of Nursing, Bengbu Medical University, Bengbu, Anhui, China.
Ling ChengCollege of Nursing, Bengbu Medical University, Bengbu, Anhui, China.
Wei WangCollege of Nursing, Bengbu Medical University, Bengbu, Anhui, China.
Zhongtao ZhouCollege of Nursing, Bengbu Medical University, Bengbu, Anhui, China.
Jing ZhangCollege of Mental Health, Bengbu Medical University, Bengbu, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The primary aim of this study is to explore distinct patterns of post-traumatic growth (PTG) and fear of cancer progression (FOP) among breast cancer patients through latent profile analysis (LPA). Additionally, we assessed the differences in demographic and disease-related factors among breast cancer patients with varying patterns. Finally, we examined the influence of socio-demographic, disease-related, social support, anxiety, depression, and post-traumatic stress disorder (PTSD) factors on the varying patterns, aiming to assist healthcare providers in developing more effective psychological care strategies for breast cancer patients. Method: A questionnaire survey was conducted on 752 breast cancer patients. Latent profile analysis was employed to explore the patterns of post-traumatic growth and fear of cancer progression in these patients, and multiple logistic regression analysis was used to identify the predictive factors for the different patterns. Results: Based on the fit indices of latent class analysis, a three-class model was identified as the optimal solution, which included the Resisting group, Struggling group, and Growth group. In the Resisting group (24.33%), patients reported low levels of post-traumatic growth and high levels of fear of cancer progression; in the Struggling group (46.14%), patients exhibited moderate levels of post-traumatic growth and low levels of fear of cancer progression; in the Growth group (29.52%), patients demonstrated high levels of post-traumatic growth and moderate levels of fear of cancer progression. Additionally, the multiple logistic regression analysis reveals that marital status, place of residence, education level, disease stage, social support, anxiety, and post-traumatic stress disorder levels in breast cancer patients serve as significant factors influencing the distinct patterns of post-traumatic growth and fear of progression. Conclusions: This study suggests that there is heterogeneity in the PTG and FOP patterns in breast cancer patients. It provides a research basis for promoting the psychological recovery of breast cancer patients and highlights the importance of focusing on the positive effects of PTG while mitigating the negative impact of FOP. Healthcare providers can implement targeted nursing interventions based on the different patterns observed in breast cancer patients.

Indexed as

breast cancer patientsfear of disease progressionlatent profile analysispositive psychologypost-traumatic growth

Identifiers

PMID41048910
PMCPMC12488680

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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.