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Patterns and Transitions of Nonsuicidal Self-Injury Outcome Expectancies and Their Associations with Nonsuicidal Self-Injury among Adolescents.

Jiaqi GuoChuhan WangJianing You
Published in: Journal of youth and adolescence (2023)
Nonsuicidal self-injury (NSSI) outcome expectancies (i.e., the expectations that certain outcomes will follow NSSI) have been confirmed to predict NSSI engagement. However, it remains unclear whether adolescents hold different patterns of NSSI outcome expectancies and therefore vary in their risks of NSSI engagement. Moreover, little is known about whether patterns transition over time, influencing the development of NSSI. Additionally, possible gender differences in the patterns and their transitions need to be explored. This study aims to address these research gaps. A total of 679 adolescents (55.8% females; M age  = 15.19, SD age  = 1.40) completed questionnaires and were surveyed semiannually for three times. Using latent profile analysis, regular latent transition analysis, and random intercept latent transition analysis, this study identified four patterns of NSSI outcome expectancies: High Affect Regulation and Moderate Negative Expectancies, Low Negative Expectancies, High Negative Expectancies, and High Communication and Negative Expectancies. The first two patterns showed high risks of NSSI, whereas the latter two patterns showed low risks of NSSI. Low Negative Expectancies was an unstable pattern. It had higher probabilities of transitioning to another high-risk pattern than transitioning to the low-risk patterns. The other three patterns had high stability. Gender had no significant effects on the four patterns or their transitions. The findings highlight the combined effects of NSSI outcome expectancies and underscore that NSSI outcome expectancies may change over time. Prevention and interventions targeting multiple factors corresponding to these expectancies should be developed.
Keyphrases
  • young adults
  • physical activity
  • metabolic syndrome
  • risk assessment
  • social media
  • type diabetes
  • adipose tissue
  • climate change
  • human health
  • weight loss
  • insulin resistance
  • neural network