Examining the generality and specificity of gender moderation in obsessive compulsive beliefs: Stacked prediction by correspondence analysis

BRITISH JOURNAL OF CLINICAL PSYCHOLOGY(2022)

引用 2|浏览8
暂无评分
摘要
Objectives In this study, we examined the degree of generality and specificity of OC beliefs are moderated by gender among individuals with OC disorders. Methods The diagnostic groups consisted of: (1) individuals with obsessive-compulsive disorder (OCD; N = 398); (2) individuals with other anxiety disorders (N = 104); and (3) undergraduate students (N = 285). To evaluate the gender moderating effect, we employed stacked prediction by correspondence analysis (CA). To conduct the analysis, we generated a two-way contingency table with rows of gender nested within the diagnostic groups and columns of OC beliefs stacked to OC symptom severity. To conduct prediction by CA of this stacked table, we considered OC beliefs as predictors and OC symptoms as outcomes. Results We confirmed with the CA results that OC belief generality, but not specificity because the OCD group members did show higher endorsement of OC beliefs compared to individuals with other anxiety disorders. Gender moderated the OC related beliefs of overestimation of threat, inflated responsibility, and intolerance of uncertainty, but not perfectionism in predicting OC symptoms. The correlational results obtained from the stacked prediction by CA further showed that as depression and anxiety increased, endorsement of OC beliefs was stronger for males than females. Discussion Clinical implications and future directions are discussed. Practitioner points OC belief generality was evident in the study but not specificity. Gender moderation was demonstrated in overestimation of threat, inflated responsibility, and intolerance of uncertainty. As depression and anxiety increased, endorsement of OC beliefs was stronger for males than females.
更多
查看译文
关键词
beck anxiety and depression inventories, obsessive-compulsive beliefs, Padua inventory, stacked prediction by correspondence analysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要