This study is examining how to customize risk assessment tools based on available jurisdiction data to improve their predictive performance. More specifically, the study will:

• Build site-specific datasets and an integrated multi-site dataset based on risk assessment data from 10 study sites.
• Isolate, test, and evaluate the relative impact of seven notable risk assessment features in each of the study sites. These include: (1) item selection technique, (2) weighting, (3) gender-specificity, (4) race-ethnicity neutrality, (5) outcome specificity, (6) prediction duration, and (7) jurisdiction variation.
• Develop optimized models for each study site's risk assessment instrument based on local data to achieve peak performance.
• Synthesize the common findings across the study sites and develop a set of recommendations about building risk assessment instruments that are best situated for local implementation and validation.

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