全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Predicting criminality from child maltreatment typologies and posttraumatic stress symptoms

DOI: 10.3402/ejpt.v4i0.19825

Keywords: Childhood maltreatment , sexual abuse , emotional abuse , posttraumatic stress disorder , latent classes , criminal behavior , national representative study

Full-Text   Cite this paper   Add to My Lib

Abstract:

Background: The associations between childhood abuse and subsequent criminality and posttraumatic stress disorder (PTSD) are well known. However, a major limitation of research related to childhood abuse and its effects is the focus on one particular type of abuse at the expense of others. Recent work has established that childhood abuse rarely occurs as a unidimensional phenomenon. Therefore, a number of studies have investigated the existence of abuse typologies. Methods: The study is based on a Danish stratified random probability survey including 2980 interviews of 24-year-old people. The sample was constructed to include an oversampling of child protection cases. Building on a previous latent class analysis of four types of childhood maltreatment, three maltreatment typologies were used in the current analyses. A criminality scale was constructed based on seven types of criminal behavior. PTSD symptoms were assessed by the PC-PTSD Screen. Results: Significant differences were found between the two genders with males reporting heightened rates of criminality. Furthermore, all three maltreatment typologies were associated with criminal behavior with odds ratios (ORs) from 2.90 to 5.32. Female gender had an OR of 0.53 and possible PTSD an OR of 1.84. Conclusion: The independent association of participants at risk for PTSD and three types of maltreatment with criminality should be studied to determine if it can be replicated, and considered in social policy and prevention and rehabilitation interventions.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413