California Department of Corrections and Rehabilitation The Parole - - PowerPoint PPT Presentation

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California Department of Corrections and Rehabilitation The Parole - - PowerPoint PPT Presentation

Jul 10, 2023 472 likes •698 views

California Department of Corrections and Rehabilitation The Parole Violation The Parole Violation Decision- -Making Instrument and the Making Instrument and the Decision California Static Risk Assessment California Static Risk Assessment

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SLIDE 1

California Department of Corrections and Rehabilitation

Steven F. Chapman, Ph.D. Assistant Secretary, Office of Research

The Parole Violation The Parole Violation Decision Decision-

  • Making Instrument and the

Making Instrument and the California Static Risk Assessment California Static Risk Assessment

Parole Violation Decision-Making Instrument Training November 15, 2008

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SLIDE 2

PVDMI PVDMI

Part of an overall strategy designed to reduce risk of recidivism, enhance success on parole, and utilize resources in the most effective manner.

  • 2006 Expert Panel

2006 Expert Panel— —California Logic Model California Logic Model

  • 2007 Rehabilitation Strike Team

2007 Rehabilitation Strike Team

  • Little Hoover Commission

Little Hoover Commission

  • Independent Review Panel

Independent Review Panel

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 3

The PVDMI: Structured The PVDMI: Structured Decision Decision-

  • Making Tool

Making Tool

  • 2008 survey of 37 parole-granting states

found that 32 (86%) used such tools.1

  • 27 used them to set conditions of parole.
  • 22 used them to determine level of

supervision.

  • 19 states are also adopting such tools to

guide responses to violations of parole.

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

1Findings From the APAI International Survey of Releasing Authorities, Association

  • f Paroling Authorities International, April 2008
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SLIDE 4

Structured Decision Structured Decision-

  • Making Tool

Making Tool

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

Low Moderate High Mandatory Referral High Most Intensive

Mandatory Referral

Moderate

Mandatory Referral

Low Least Intensive

Mandatory Referral

Violation Severity Risk Level

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SLIDE 5

Violation Severity Violation Severity

  • Technical and Non-Technical violations

are rated for severity.

  • Ratings are based upon Board of Parole

Hearing Standards.

  • Sex Offenders and High-Risk Sex

Offenders have appropriate enhancements.

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 6

Risk to Re Risk to Re-

  • Offend

Offend

  • DAPO required a risk assessment that

was validated on California offenders.

  • COMPAS was years away from validation.
  • Pressure was on to produce a solution in

weeks or months rather than years.

  • Decision made to construct the

Decision made to construct the California Static Risk Assessment (CSRA). California Static Risk Assessment (CSRA).

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 7

Constructing the CSRA Constructing the CSRA

  • Combined effort of:

– CDCR Division of Parole Operations – CDCR Research – The 2007 Rehabilitation Strike Team – UCI Center for Evidence-Based Corrections – Washington State Institute for Public Policy – Assessments.com – UCLA

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 8

California Static Risk California Static Risk Assessment Assessment

  • Computes the risk to re-offend.
  • Uses static risk indicators—characteristics

that do not change—to predict risk.

– Gender – Age – Offense History

  • The CSRA is an actuarial risk tool.

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 9

How Actuarial Risk Prediction Works How Actuarial Risk Prediction Works

  • Insurers want to know the likelihood that a

driver will be in an accident

  • They use their extensive records data to

determine what factors are related to drivers experiencing an accident

  • The model:

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

Risk (accident)=age + gender + zip code + prior accident history Risk (accident)=age + gender + zip code + prior accident history + +… …etc. etc.

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SLIDE 10

CSRA CSRA Foundations Foundations

  • Level of Service Inventory—Revised
  • Modifications by Robert Barnoski of

WSIPP

  • Elements of the COMPAS
  • These tools provided the “starting point”

for the development of the CSRA

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 11

Construction and Validation Construction and Validation

  • CDCR research selected 103,000
  • ffenders released in FY 2002-03.
  • DOJ matched offenders to their arrest

histories, prior and subsequent to release.

  • OISB provided additional offender

characteristics data.

  • DJJ matched for juvenile histories.
  • DAPO provided violation information.

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 12

Development of the Model Development of the Model

  • Sample divided randomly into construction

and validation groups.

  • Developed items and weights on the

construction group.

  • Validated instrument on the validation

group.

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 13

Results: 22 Items to Predict Results: 22 Items to Predict Recidivism Recidivism

  • Demographics: Age at release, gender
  • Number of felony sentences
  • Felony sentences for murder/ manslaughter,

sex, violent, weapons, property, drug and escape offenses

  • Misdemeanor sentences for assault, sex,

weapons, property, drug, alcohol and escape

  • ffenses
  • Revocations of probation or parole supervision

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 14

What does the CSRA predict? What does the CSRA predict?

Measures of Re Measures of Re-

  • offending
  • ffending
  • Arrest

– Captures the most criminal behavior. – Most likely to “over-capture.”

  • Conviction

– Highest standard of proof. – In California, instances of criminal behavior do not always result in conviction for a new offense.

  • Return to Custody

– Most direct impact on institutions population.

CSRA uses arrest as the outcome CSRA uses arrest as the outcome--

  • -most conservative

most conservative

  • utcome for protection of public safety.
  • utcome for protection of public safety.

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 15

CSRA Scoring CSRA Scoring

  • Low Risk
  • Moderate Risk
  • High Risk in Three Categories:

– Violent Re-offending – Property Re-offending – Substance Abuse Re-offending

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 16

CSRA Scores and Recidivism CSRA Scores and Recidivism

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

48 69 82 82 82 12 17 28 15 13 8 15 18 29 17 10 21 22 25 37 10 20 30 40 50 60 70 80 90 100 Low (22%) Moderate (33%) High Drug (9%) High Property (19%) High Violent (17%) "Most Serious Arrest" Rates by Risk Group Percent Any Felony Drug Felony Property Felony Violent Felony

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Accuracy Accuracy

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

Instrument AUC Sample Recidivism Measure Source

CSRA CSRA 0.70 0.70 103,000 103,000 releases releases Felony arrest Felony arrest Current Current COMPAS 0.67 515 California parolees Return to prison Farabee and Zhang (2007) Criminal History Computation 0.68 28,519 Federal

  • ffenders

Re-conviction, re-arrest w/out dispo. available, supervision revocation US Sentencing Commission (2004) LSI-R 0.67 22,533 Wash.

  • ffenders

Any conviction WSIPP (2003) Washington Static Risk Assessment 0.74 51,648 Wash.

  • ffenders

Felony conviction WSIPP (2007

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SLIDE 18

Weaknesses Weaknesses

  • Decisions are based on aggregate, or

group, performance.

  • Does not include dynamic (changeable)

factors.

  • May be “blind” to important factors that

may make a difference, positively or negatively, for the individual.

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 19

Strengths Strengths

  • Promotes efficiency, consistency and
  • bjectivity in decision-making
  • Has an empirical basis
  • Is more accurate than practitioner

judgment alone

  • Best results when combined with

practitioner judgment

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation

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SLIDE 20

Importance of Overrides Importance of Overrides

  • No actuarial tool is 100% accurate all the

time.

  • Overrides are expected.
  • Question: How many is enough? How

many is too much?

Office of Research July 14, 2009 California Department of Corrections and Rehabilitation