A network analysis of ICD-11 complex PTSD symptoms in the treatment-seeking population in Iran

The study determined that feelings of failure and worthlessness are the most central symptoms in the Complex PTSD network. It was suggested that these symptoms should be given priority in theoretical and treatment models of Complex PTSD.

Authors

Institutions

Publication Details

Journal
BMC Psychiatry
Published
2025-03-04
DOI
https://doi.org/10.1186/s12888-024-06444-1
Citations
1
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
2.12
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A network analysis of ICD-11 complex PTSD symptoms in the treatment-seeking population in Iran

Hojjatollah Farahani, Ahmad Ashouri, Fahimeh Fathali Lavasani, Zahra Omidbakhsh et al.
1 citations
BMC Psychiatry
Mental Health Research Topics
2.12
article

A network analysis of ICD-11 complex PTSD symptoms in the treatment-seeking population in Iran

Hojjatollah Farahani, Ahmad Ashouri, Fahimeh Fathali Lavasani, Zahra Omidbakhsh, Zahra Mohammadi, Mahmood Dehghani
article en
1 citations

Abstract

The study determined that feelings of failure and worthlessness are the most central symptoms in the Complex PTSD network. It was suggested that these symptoms should be given priority in theoretical and treatment models of Complex PTSD.

BMC PsychiatryVol. 25(1)
Tarbiat Modares University (IR), Iran University of Medical Sciences (IR), Shiraz University (IR)
Good health and well-being
Openalex Percentile: Top 16%
Mental Health Research Topics
2.12
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.