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The dimensionality of perceptual anomalies and their relationships with bullying victimization among Chinese adolescents: From a network perspective.

Xiaoqi SunJingyi Zhong
Published in: Schizophrenia research (2023)
Anomalous perceptual experiences in adolescents are common and may predict future psychotic disorders and other psychopathologies. However, the underlying structure and their specific relationships with bullying victimizations, a typical stressor for adolescents, remain unclear. Therefore, the current study aimed to clarify the structure of perceptual anomalies as assessed by the Cardiff Anomalous Perceptions Scale (CAPS) using exploratory graph analysis (EGA), a new factor retention method based on network psychometrics. The second aim was to explore whether specific dimensions of perceptual anomalies are particularly associated with certain forms of bullying victimization. Data from a validated sample of 1199 Chinese adolescents (56.0 % females, age range: 14-20) on perceptual anomalies and bullying victimizations were analyzed using network approaches, including EGA and mixed graphical modeling (mgm). Results showed that each anomalous perception was experienced by 13.8-50.3 % of the participants. EGA identified four dimensions: aberrant bodily perceptions, altered daily experiences, chemosensation (i.e., abnormal gustatory and olfactory experiences), and clinical psychosis (i.e., visual and auditory hallucinatory experiences). Among them, the altered daily experiences dimension possessed the highest centrality. Physical bullying and cyberbullying were directly and positively linked to two of the aberrant bodily experiences. Bootstrap analyses suggest that the results are reliable. The current findings support the existence of multiple contributive factors to perceptual anomalies and underscore the importance of bullying prevention in reducing mental health risks for adolescents, particularly the risk of psychosis.
Keyphrases
  • physical activity
  • working memory
  • young adults
  • mental health
  • high school
  • bipolar disorder
  • big data
  • current status
  • network analysis
  • neural network