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For example, the PCL-R commonly used by forensic psychologists is less consistent with classic theories of psychopathy than “pseudo-psychopathy”—i.e., antisocial features and mental health problems that can lie downstream from trauma.

 

This study is forthcoming in Assessment and available here. The citation is Kennealy, P. J., Skeem, J. L., & Lilienfeld, S. O. (In Press) Clarifying Conceptions Underlying Adult Psychopathy Measures: A Construct Validity Metric Approach. Assessments

 

Abstract

Although the Psychopathy Checklist-Revised (PCL-R) and Psychopathic Personality Inventory (PPI) ostensibly measure the same construct, they seem to emphasize different conceptions of psychopathy. This study was designed to clarify these differences by testing how well the PCL-R and PPI map alternative conceptions of psychopathy. Construct validity metrics were used to compare patterns of associations between psychopathy measures and 14 theory-relevant criterion variables that were observed in a sample of 1,281 offenders—with patterns of associations that were predicted based on alternative psychopathy conceptions. PCL-R total scores were most consistent with Karpman’s affective dysfunction-centered secondary conception, and PPI total scores were most consistent with the McCords’ lovelessness-based conception. Although similarities emerged at the factor level, the PPI demonstrated higher levels of consistency between theory-based predictions and observed relations than did the PCL-R. These results provide direction for refining measures in future research and interpreting PCL-R and PPI scores in current practice.

Keywords: psychopathy, Psychopathy Checklist-Revised, Psychopathic Personality Inventory, construct validity

Leah Jacobs and Jennifer L. Skeem's study on this topic is forthcoming in American Journal of Community Psychology and available here.

Pre-print available behind paywall here.

 

Abstract

Justice-involved people vary substantially in their risk of reoffending. To date, recidivism prediction and prevention efforts have largely focused on individual-level factors like antisocial traits. Although a growing body of research has examined the role of residential contexts in predicting reoffending, results have been equivocal. One reason for mixed results may be that an individual’s susceptibility to contextual influence depends upon his or her accumulated risk of reoffending. Based on a sample of 2,218 people on probation in San Francisco, California, this study draws on observational and secondary data to test the hypothesis that individual risk moderates the effect of neighborhood factors on recidivism. Results from survival analyses indicate that individual risk interacts with neighborhood concentrated disadvantage and disorder—these factors increase recidivism among people relatively low in individual risk, but not those at higher risk. This is consistent with the disadvantage saturation perspective, raising the possibility that some people classified as low risk might not recidivate but for placement in disadvantaged and disorderly neighborhoods. Ultimately, residential contexts “matter” for lower risk people and may be useful to consider in efforts to prevent recidivism.

 

 

Keywords: risk assessment, recidivism, disadvantage saturation, neighborhood effects, disadvantage, disorder

Jennifer L. Skeem and Devon L. L. Polaschek publication "High risk, not hopeless: correctional intervention for people at risk for violence" ,discussing effective ways to identify and reduce risk of reoffending for people at high risk of recidivism, is highlighted in a new Marquette Law Review special symposium issue on preventing violent reoffending, 

As part of Perspectives, the Journal of the America Probation and Parole Association, Jennifer Eno Louden, Rebekah Adair and Jennifer L. Skeem discuss myths regarding people with mental illness and provide guidelines for best practices in a community correctional context in "Moving Past the Myths: Research- Informed Practices for Supervising Clients with Serious Mental Illness".  

Congratulations to our amazing Sonoma research assistant Kathryn Schmidt for her acceptance into the Masters in Clinical Research program at UCSD for Summer of 2020. While we will miss her greatly, we can’t wait to see all the great things she will accomplish in the future and we wish her the best of luck!

As part of Christopher Slobogin’s Special Issue on Implementation of Post-Conviction Risk Assessment, Jennifer Skeem and Christopher Lowenkamp analyze how alternative ways for "debiasing" risk assessment algorithms affect various tradeoffs in their article "Using Algorithms to Address Trade-Offs Inherent in Predicting Recidivism"

Zhiyuan “Jerry” Lin, Jongbin Jung, Sharad Goel and Jennifer Skeem discuss their findings in attempting to replicate and extend the 2018 Dartmouth study in the Washington Post article "In the U.S. criminal justice system, algorithms help officials make better decisions, our research finds". 

Listen to Jennifer Skeem discuss how artificial intelligence aids in risk assessments in a recent interview with Robyn Williams from The Science Show.

 

https://www.abc.net.au/radionational/programs/scienceshow/jennifer-skeem/11988280

In their article "Lost in Translation: 'Risks' 'Needs,' and 'Evidence' in Implementing the First Step Act" Jennifer Skeem and John Monahan anaylze two problematic issues in the manner in which the First Step Act, a federal prison reform bill, is being implemented. 

Professor Skeem’s newest paper — “The limits of human predictions of recidivism” — was published Feb. 14, 2020, in Science Advances. With her Stanford-based coauthors, Skeem presented the research on Feb. 13 in a news briefing at the annual meeting of the American Association for the Advancement of Science (AAAS) in Seattle, Wash.  For details, see the Berkeley News article "Algorithms are better than people in predicting recidivism, study says."