Treatment of Sex Offenders

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Table 2.1 Glueck and Glueck’s ( 1930 ) risk factors 1930— 500 Criminal Careers 1934— 1000 Juvenile Delinquents Family – Official record of arrest among family members – Economic hardship – Poor parental education – Abnormal or unhealthy home situation – Long/complete absence of parents – Intemperate (excessive use of alcohol, violent behavior) – Immoral mother – Foreign or mixed parentage – Child moved about during childhood or adolescence – Child left parental home prior to sentence – Very large family – Poor parental education – Parents separated/divorced – Parents unduly quarrelsome – Insufficient parental supervision/discipline – Parents’ excessive use of alcohol/violence – Parents’ immoral, foreign, or mixed marriage School – Educational retardation – Educational retardation – Leaving school early in life Lifestyle – Elicits heterosexual relations – Gambling – Alcoholism – Keeping bad company – Infrequent church attendance Personality/ intelligence – Open conflict with social authorities—school/law – Dull or borderline intelligence – Psychotic or psychopathic – Neuropathic traits – Extreme suggestibility – Emotional instability – Impulsiveness – Truancy 1950— Unraveling Juvenile Delinquency – Hanging around streets during leisure time – Restless – Impulsive – Extroverted – Aggressive – Destructive – Subnormal intelligence – Marked emotional and personality handicaps Employment – Unskilled/semiskilled work prior to incarceration – Unskilled/semiskilled work prior to incarceration – Insufficient parental discipline – Poor supervision – Very large family – Low family cohesiveness – Hostile – Defiant – Resentful – Suspicious – Stubborn – Assertive – Adventurous – Not submissive to authority, tendency to think in concrete not abstract terms
2 A Brief History of Sexual Offender Risk Assessment 23 population, the corresponding background factor was considered a favorable one. All positive factors were incorporated into an experience table, and a candidate for parole was assigned one point for each favorable factor in his background. A table giving the violation rate for offenders with different numbers of favorable factors was derived for the population under study. Burgess ( 1928 ) found that an experience table was more accurate than a probation officer’s predictions of parole success. Similarly, Sarbin ( 1942 ) and Wittman and Steinberg ( 1944 ) also found that statistical methods are more accurate in outcome predictions. Sarbin ( 1944 ) argued a priori for the superiority of mechanical prediction, as not doing justice to the potential flexibility of clinical judgments and that the clinician engages in a weighting-andadding process used in statistical prediction formulas, but clinicians calculate less reliably and so are less accurate (Grove & Lloyd, 2006 ). Although proponents of the clinical judgment continued to emphasize the value of professional intuition, the emphasis in risk prediction slowly changed following Meehl’s ( 1954 ) publication. Meehl’s ( 1954 ) seminal book made four major contributions to the clinical-statistical debate which was to advance the field beyond recognition: 1. Distinguished data gathering from data combination, focusing on the accuracy of clinical versus mechanical methods for combining data. 2. A convincing refutation that the clinical-statistical antithesis is artificial, although in his later work he argued against using both together (Grove & Meehl, 1996 ). 3. A recognition of a clinician’s potential for creative insight. 4. Comparisons of clinical and statistical prediction strongly favored statistical prediction. We will now consider the impact of Paul E. Meehl’s contribution to the field of risk prediction in the development of ARAIs for sexual offenders. Risk Estimation Through Mechanical Prediction Meehl ( 1954 ) encouraged applying actuarial prediction to clinical assessment in the 1950s. He was one of the first to introduce the idea of mechanical risk assessment tools based on known recidivism factors. Here, Meehl ( 1954 ) suggested that mechanical risk assessment tools have explicit item rules as well as clear definitions for combining the item scores into a total score. Using experience tables, these total scores are then translated into probabilistic estimates of risk, a core feature of his definition of an actuarial risk assessment procedure and a method still employed in several modern ARAIs (Hanson & Thornton, 2000 ; Thornton et al., 2003 ). Meehl and Rosen ( 1955 ) were among the first to consider the effect of base rates on prediction and adequate sampling methodology in order to enhance predictive accuracy. All actuarial risk instruments are ultimately derived from base rates, which are usually recorded as reoffense or recidivism. So a base rate of 10 % usually means that 10 % of a group of sexual offenders can be expected to reoffend
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Chapter 4 Risk Formulation: The New
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Chapter 6 Polygraph Testing of Sex
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6 Polygraph Testing of Sex Offender
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Chapter 7 The Strengths of Treatmen
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7 The Strengths of Treatment for Se
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Chapter 8 Responsivity Dynamic Risk
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8 Responsivity Dynamic Risk Factors
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Chapter 9 Early Detection and Inter
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9 Early Detection and Intervention
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Chapter 10 Community Control of Sex
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10 Community Control of Sex Offende
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Chapter 12 The Shortcomings of Sexu
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12 The Shortcomings of Sexual Offen
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Treatment of Sex Offenders

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