Improving Decision Making in Forensic Child Sexual Abuse Evaluations.

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Abstract

Mental health professionals can assist legal decision makers in cases of allegations of child sexual abuse by collecting data using forensic interviews, psychological testing, and record reviews, and by summarizing relevant findings from social science research. Significant controversy surrounds another key task performed by mental health professionals in most child sexual abuse evaluations, i.e., deciding whether or not to substantiate unconfirmed abuse allegations. The available evidence indicates that, on the whole, these substantiation decisions currently lack adequate psychometric reliability and validity: an analysis of empirical research findings leads to the conclusion that at least 24% of all of these decisions are either false positive or false negative errors. Surprisingly, a reanalysis of existing research also indicates that it may be possible to develop reliable, objective procedures to improve the consistency and quality of decision making in this domain. A preliminary, empirically-grounded procedure for making substantiation decisions is proposed.

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Clinical versus mechanical prediction: a meta-analysis.

William M. Grove, David H. Zald, Boyd S. Lebow … (2000)

The process of making judgments and decisions requires a method for combining data. To compare the accuracy of clinical and mechanical (formal, statistical) data-combination techniques, we performed a meta-analysis on studies of human health and behavior. On average, mechanical-prediction techniques were about 10% more accurate than clinical predictions. Depending on the specific analysis, mechanical prediction substantially outperformed clinical prediction in 33%-47% of studies examined. Although clinical predictions were often as accurate as mechanical predictions, in only a few studies (6%-16%) were they substantially more accurate. Superiority for mechanical-prediction techniques was consistent, regardless of the judgment task, type of judges, judges' amounts of experience, or the types of data being combined. Clinical predictions performed relatively less well when predictors included clinical interview data. These data indicate that mechanical predictions of human behaviors are equal or superior to clinical prediction methods for a wide range of circumstances.