A Bayesian Decision-Support Tool for Child Sexual Abuse Assessment and Investigation

Alessandro Tadei, Johan Pensar, Jukka Corander, Katarina Finnilä, Pekka Santtila, Jan Antfolk

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)

Abstract

In assessments of child sexual abuse (CSA) allegations, informative background information is often overlooked or not used properly. We therefore created and tested an instrument that uses accessible background information to calculate the probability of a child being a CSA victim that can be used as a starting point in the following investigation. Studying 903 demographic and socioeconomic variables from over 11,000 Finnish children, we identified 42 features related to CSA. Using Bayesian logic to calculate the probability of abuse, our instrument—the Finnish Investigative Instrument of Child Sexual Abuse (FICSA)—has two separate profiles for boys and girls. A cross-validation procedure suggested excellent diagnostic utility (area under the curve [AUC] = 0.97 for boys and AUC = 0.88 for girls). We conclude that the presented method can be useful in forensic assessments of CSA allegations by adding a reliable statistical approach to considering background information, and to support clinical decision making and guide investigative efforts.

Original languageUndefined/Unknown
Pages (from-to)374-396
JournalSexual Abuse
Volume31
Issue number4
DOIs
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • child sexual abuse investigations
  • child sexual abuse
  • multimodal assessment
  • Bayesian logic
  • Decision making

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