Revisiting the effectiveness of HOPE/swift-certain-fair supervision programs: A meta-analytic review

CRIMINOLOGY & PUBLIC POLICY(2024)

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摘要
Research SummaryOriginated nearly two decades ago in Hawaii by Judge Steven Alm, a community supervision-court model known as "Project HOPE" proposed to reduce probation failure by responding to violations with immediate but short jail terms. Despite negative evidence from Lattimore and colleagues' 2016 Demonstration Field Experiment (DFE) across four locations, advocates continued to trumpet programs based on Project HOPE's core principles of swift, certain, and fair (SCF) sanctions, arguing that these deterrence-oriented interventions-now known under the acronym SCF programs-reduce recidivism. To assess this claim, a meta-analysis was conducted of 18 studies reporting on 24 separate evaluations of programs falling under the Project HOPE/SCF umbrella. The analysis revealed that the intervention had a statistically significant but substantively small impact on recidivism (the main overall effect = -.058). Moderator analyses revealed weak to null findings across variations in methodological and HOPE/SCF program characteristics. Policy ImplicationsAt present, evaluation evidence is weak and not robust enough to support the continued government funding and implementation of SCF programs in their current form on grounds of recidivism reduction. Such deterrence-oriented programs may be based on a flawed theory of recidivism that fails to identify criminogenic risk factors for change. SCF programs might prove more effective if integrated with treatment modalities, though this remains to be demonstrated. More broadly, a range of community supervision approaches now exist that emphasize building relationships with individuals under supervision and guiding their prosocial development. These alternatives might offer a more promising avenue for reform than current programs based on SCF principles.
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关键词
community supervision,correctional effectiveness,Project HOPE,swift-certain-fair programs
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