The COVID-19 pandemic has resulted in negative mental health outcomes throughout the world, and its impact on social interactions and relationships is likely to be evident in problematic social anxiety. This systematic review qualitatively synthesized data from studies that have reported on the effects of the pandemic on social anxiety. A systematic search of Web of Science Core Collection, Embase, PsychINFO, Scopus, EBSCOhost, Cochrane Central Register of Controlled Trials, and Proquest Central—Dissertations and Theses was conducted, with thirty-three studies meeting the inclusion criteria. The results suggest that social anxiety has been heightened in the general population due to the pandemic, with women and low-income earners being especially vulnerable. Other contributing factors include impaired coping strategies, lower socio-emotional well-being, limited support networks, and contraction of the SARS-CoV-2 virus. Individuals with a Social Anxiety Disorder diagnosis may be at risk of a deterioration of mental health in general. Limitations of the literature reviewed include the predominance of cross-sectional study designs, which limit causal inferences are limited. Additionally, associations may be inflated as many studies have not accounted for mediating variables. Taken together, the research suggests that social anxiety, either pre-pandemic or arising due to the pandemic environment, has contributed to a variety of negative mental health outcomes related to social anxiety.

This systematic review and meta-analysis aimed to assess complex post-traumatic stress disorder (CPTSD) pooled prevalence across global and specific populations (e.g., military, clinical) and examined potential gender differences. We systematically searched five major databases (Web of Science, Scopus, PsycNET, Psych and behavioural sciences collection, PubMed) up to 31/01/2025 for peer-reviewed articles reporting CPTSD prevalence using validated ICD-11 assessments. Articles were assessed for quality using the JBI prevalence checklist; no studies were excluded. In total 138,681 participants from 167 studies were analysed. Gender-specific analyses were conducted where prevalence was reported by gender. A random-effects model with the meta package in R estimated the global pooled prevalence of CPTSD at 6.2 % (95 % CI [3.7 %, 10.3 %]). Prevalence varied across specific trauma-exposed population groups; highest in clinical (44.7 %), domestic violence/sexual abuse survivors (40.0 %), and military (36.4 %) samples, and lowest in emergency services (7.4 %). No gender difference in prevalence was observed. Findings suggest the support systems or selection processes inherent in emergency services may lower CPTSD risk. Conversely, extreme trauma, limited support, or cultural factors may explain elevated prevalence in military samples. High CPTSD prevalence in clinical samples highlight trauma's pervasive impact, underscoring the need for targeted treatment addressing both CPTSD and comorbidities (e.g., mood, anxiety disorders). The absence of gender differences contrasts with typically higher PTSD rates in women. Limitations include inconsistent trauma definitions and lack of clinician-administered instruments. Findings highlight the need for standardised data collection and reporting, gender-specific reporting, and recognition of CPTSD as a comorbidity in clinical settings.

The International Trauma Questionnaire (ITQ) is a widely used diagnostic tool for post-traumatic stress disorder (PTSD) and complex PTSD (CPTSD). Although findings are mixed, investigations of the ITQ’s internal structure have most often supported a six-factor first-order model and a two-factor second-order model. This study utilized meta-analytic factor analysis to investigate the ITQ’s latent structure. A systematic search of PsycINFO, Web of Science, PubMed, MEDLINE, Cochrane, Scopus, CINAHL, and ProQuest Dissertations was conducted, with 57 studies (total N = 43,066) included in the final analysis. A two-stage meta-analytic structural equation modeling approach was implemented which pooled correlation matrices and performed confirmatory factor analyses. The two-factor second-order model demonstrated good fit, emphasizing its clinical relevance. However, a seven-factor correlated first-order model demonstrated superior fit, consistently outperforming other models across ITQ versions, symptom severity levels, English versus translated versions, and both PTSD and non-PTSD samples. This model indicates that CPTSD encompasses a broad range of symptoms and specifically that the distinction between affective hyperactivation and hypoactivation is important to progress the construct validity of CPTSD. Additionally, reliability analyses revealed limited internal consistency at the subscale level. These findings highlight the need for comprehensive assessments and refined item content to enhance CPTSD measurement.

Adverse childhood events and insecure attachment lead to difficulties in emotion regulation and social support, predisposing individuals to post‐traumatic stress disorder (PTSD) and complex PTSD (CPTSD) when exposed to potentially traumatic events. However, the field lacks an integrated account of how these risk and protective factors combine, including the pathways through which attachment‐related vulnerability contributes to trauma symptoms. This study tested a proposed intra‐interpersonal vulnerability model using structural equation modelling with a sample of 706 participants ( M = 30.64 years, SD = 11.00). Model fit was strong for symptoms of ICD‐11 PTSD (CFI = 0.969; SRMR = 0.055) and ICD‐11 CPTSD (CFI = 0.976; SRMR = 0.056). The model explained 35% of variance in PTSD symptoms and 75.7% of variance in CPTSD symptoms. Cross‐validation supported the generalisability of the model, with the overall pattern of effects also replicated among participants with elevated PTSD/CPTSD symptoms. As expected, adverse childhood events were linked to attachment insecurity (e.g., β = 0.29), which influenced emotional identification, emotion dysregulation and social connectedness ( β = 0.24 to β = −0.69). Contrary to expectations attachment insecurity did not impact either PTSD or CPTSD trauma symptoms directly. Emotion dysregulation was significantly associated with both PTSD and CPTSD symptoms, whereas social connectedness was associated with CPTSD but not PTSD symptoms. Overall, the results indicate that risk and protective factors build upon each other, reinforcing the importance of developmental aspects in the emergence of post‐traumatic stress. Limitations include a community sample and cross‐sectional data, suggesting a need for longitudinal research.

Meta-analytic confirmatory factor analysis (CFA) is a type of meta-analytic structural equation modeling (MASEM) that is useful for evaluating the factor structure of measurement scales based on data from multiple studies. Modeling the factor structure is just one example of many potentially interesting research questions. Analyzing covariance matrices allows for the evaluation of measurement properties across studies, such as whether indicators are functioning the same across studies. For example, are some indicators more indicative of the common factor in certain types of studies than in others? The additional analysis of means of the observed variables opens up many other research questions to consider such as: ‘Are there mean differences in mental health between clinical and non-clinical samples?’. To answer such questions, it is necessary to analyze both the covariance and the mean structure of the indicators. Meta-analytic CFA with means is applicable when the studies included in the meta-analysis used the same indicators, measured on the same scales. In this paper, we present and illustrate a method to incorporate the means of variables in MASEM analyses of such datasets. We focus on meta-analytic CFA, with the aim of testing differences in latent means across studies. We provide illustrations of the comparison of latent means across groups of studies using two empirical datasets, for which data and analysis scripts are provided online. Finally, we discuss how the proposed method relates to other analysis options such as multigroup or multilevel structural equation modeling.