Evidence map›Paper›PMID 28475593›Full record

ArticlePloS one2017

Association between social support and post-traumatic stress disorder symptoms among Chinese patients with ovarian cancer: A multiple mediation model.

Chunli Liu, Yi Zhang, Hong Jiang, Hui Wu

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Characteristics and influencing factors of demoralization in ovarian cancer: a latent profile analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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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

4 authors.

Chunli LiuLibrary of China Medical University, Shenyang, Liaoning, China.
Yi ZhangThe First Affiliated Hospital of China Medical University, Heping District, Shenyang, Liaoning, China.
Hong JiangSchool of Public Health, China Medical University, Shenyang, Liaoning, China.
Hui WuDepartment of Social Medicine, College of Public Health, China Medical University, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Post-traumatic stress disorder (PTSD) symptoms can develop after person experiences one or more traumatic events. Little research, however, has been done on PTSD symptoms of patients with ovarian cancer. The present study aimed to estimate the prevalence of PTSD symptoms in patients with ovarian cancer in China; the effects of demographic and clinical variables on PTSD symptoms; multiple mediation roles in the association between social support and PTSD symptoms in patients with ovarian cancer in China. We collected demographic and clinical information of patients with ovarian cancer in the first and second hospitals of China Medical University between January 1, 2014 and December 31, 2015. Qualified patients were asked to complete the Posttraumatic Stress Disorder Checklist-Civilian Version (PCL-C), Duke-UNC Functional Social Support Questionnaire, Herth Hope Index (HHI), and Resilience Scale-14 (RS-14). 201 patients provided responses. We performed hierarchical linear regression to assess the correlation between social support and PTSD symptoms and bootstrapping to test the mediating role of hope and resilience as potential mediators. After controlling demographic and clinical characteristics, social support negatively correlated with PTSD symptoms (β = -0.406, P < 0.01). Social support explained 14.7% of the variance in PTSD symptoms. Hope and resilience explained 17.0% of the variance in PTSD symptoms. The proportion of the hope mediating effect was 43.37% for social support and the proportion of the resilience mediating effect was 10.64% for social support. Hope and resilience partly mediated the correlation between social support and PTSD symptoms despite accounting for different proportions of the mediating effect. Future intervention plans should pay more attention to social support as well as hope and resilience to prevent, relieve and treat PTSD symptoms.

Indexed as

Models, PsychologicalSocial SupportChinaFemaleHopeHumansMiddle AgedOvarian NeoplasmsResilience, PsychologicalStress Disorders, Post-Traumatic

Identifiers

PMID28475593
PMCPMC5419605

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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.