Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch007
The 2004-2014 findings from the District of Columbia Comprehensive Assessment System (DC CAS) and the National Assessment of Educational Progress showed that the District's 4th and 9th graders scored 49th out of 51 states and territories in 2016. The District had switched to the federal PAARC test, and in 2017 it began to implement the Integrated Basic Education and Skills Training (I-BEST) model. Implementing this model means that students will work with two teachers in the classroom: one provides job-training and another who teaches basic skills in reading, math, or English language. The students' historically-low test scores and the implementation of the I-BEST model suggest that CSOSA clients referred to the District's public and charter schools or nonprofit adult education contractors would have been unlikely to have been able to obtain a high school degree or GED credential.
{"title":"Adult Education Need vs. Capacity","authors":"","doi":"10.4018/978-1-7998-1147-3.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch007","url":null,"abstract":"The 2004-2014 findings from the District of Columbia Comprehensive Assessment System (DC CAS) and the National Assessment of Educational Progress showed that the District's 4th and 9th graders scored 49th out of 51 states and territories in 2016. The District had switched to the federal PAARC test, and in 2017 it began to implement the Integrated Basic Education and Skills Training (I-BEST) model. Implementing this model means that students will work with two teachers in the classroom: one provides job-training and another who teaches basic skills in reading, math, or English language. The students' historically-low test scores and the implementation of the I-BEST model suggest that CSOSA clients referred to the District's public and charter schools or nonprofit adult education contractors would have been unlikely to have been able to obtain a high school degree or GED credential.","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123318083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch005
The District's Homeward DC strategic housing plan projected that, by 2020, homeless people would remain in shelter care for less than 60 days and that the number of shelter care families would drop from 1,200 families in 2014 to 435 in 2020 (a 65% reduction in five years). The strategic plan also involved closing a massive building complex that was the city's largest and oldest family homeless shelter. Closing the building required securing contracts and permits for the development of smaller homeless shelters in nearly every ward. Numerous delays in shelter development prevented the successful implementation of the District's strategic housing plan, and a local audit determined that the there was no oversight of the hotels housing the homeless. The delays and the local auditor's report suggested that CSOSA clients referred to the District's housing programs would have been unlikely to have received housing.
{"title":"Shelter and Affordable Housing Need vs. Capacity","authors":"","doi":"10.4018/978-1-7998-1147-3.ch005","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch005","url":null,"abstract":"The District's Homeward DC strategic housing plan projected that, by 2020, homeless people would remain in shelter care for less than 60 days and that the number of shelter care families would drop from 1,200 families in 2014 to 435 in 2020 (a 65% reduction in five years). The strategic plan also involved closing a massive building complex that was the city's largest and oldest family homeless shelter. Closing the building required securing contracts and permits for the development of smaller homeless shelters in nearly every ward. Numerous delays in shelter development prevented the successful implementation of the District's strategic housing plan, and a local audit determined that the there was no oversight of the hotels housing the homeless. The delays and the local auditor's report suggested that CSOSA clients referred to the District's housing programs would have been unlikely to have received housing.","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch002
The percentage of recidivism reduction projected to reduce recidivism by each social service or intervention is presented. Meta-analysis is used to determine these projections. However, in the last few years meta-analysis methods have been questioned. The most formidable criticism of meta-analysis is found in a master study of the statistical power of studies in criminology. This master study (of over 8,000 studies) found that about 25% of studies in criminology exhibited high statistical power (in the 0.99 to 1.00 range). However, the study also found about 25% of studies had power between 0.01 and 0.24. These findings suggested that roughly a fourth of all studies in criminology have levels of statistical power that make it nearly impossible to identify the effects they are estimating. In other words, this chapter questions whether we should be confident in the recidivism reduction projections for various interventions.
{"title":"Meta-Analysis and Social Services Interventions","authors":"","doi":"10.4018/978-1-7998-1147-3.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch002","url":null,"abstract":"The percentage of recidivism reduction projected to reduce recidivism by each social service or intervention is presented. Meta-analysis is used to determine these projections. However, in the last few years meta-analysis methods have been questioned. The most formidable criticism of meta-analysis is found in a master study of the statistical power of studies in criminology. This master study (of over 8,000 studies) found that about 25% of studies in criminology exhibited high statistical power (in the 0.99 to 1.00 range). However, the study also found about 25% of studies had power between 0.01 and 0.24. These findings suggested that roughly a fourth of all studies in criminology have levels of statistical power that make it nearly impossible to identify the effects they are estimating. In other words, this chapter questions whether we should be confident in the recidivism reduction projections for various interventions.","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126895116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch006
The objectives of a local audit were to assess whether the local employment services agency had developed and implemented sufficient internal controls to provide efficient and effective management of the District's 12 employment programs. The audit found that the employment agency did not establish written performance criteria for its employment programs or provide adequate oversight of its programs and that, without performance criteria, the agency could not accurately measure the quality and effectiveness of training services or gauge whether programs were effective or whether program objectives had been met. Further, from 2009-2017, the employment agency experienced sustained performance issues resulting in an “at-risk” designation by Department of Labor. Negative audit reports from both local and federal auditors suggested that CSOSA clients referred to the District's employment programs would have been unlikely to have received employment services.
{"title":"Employment Need vs. Capacity","authors":"","doi":"10.4018/978-1-7998-1147-3.ch006","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch006","url":null,"abstract":"The objectives of a local audit were to assess whether the local employment services agency had developed and implemented sufficient internal controls to provide efficient and effective management of the District's 12 employment programs. The audit found that the employment agency did not establish written performance criteria for its employment programs or provide adequate oversight of its programs and that, without performance criteria, the agency could not accurately measure the quality and effectiveness of training services or gauge whether programs were effective or whether program objectives had been met. Further, from 2009-2017, the employment agency experienced sustained performance issues resulting in an “at-risk” designation by Department of Labor. Negative audit reports from both local and federal auditors suggested that CSOSA clients referred to the District's employment programs would have been unlikely to have received employment services.","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127062016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch003
Since 2009, more than 840 Second Chance Act grant awards have been made to government and nonprofit agencies, and taxpayers have paid nearly 700 million dollars in Second Chance grants. Additionally, $154 million has been spent on probation and parole supervision agencies and staff through the Justice Reinvestment Initiative. Yet, our probation and parole population continue growing! Given the amount of money taxpayers have invested in programs, it seems nothing works. In the 20th century, it was assumed that the use of randomized and control-group research designs and complex statistical analysis and state-of-the-art computer software would be sufficient to find what “works.” But, we have not yet found what “works.” This chapter asks two questions: 1) Is it the case that “nothing works”? or 2) Is it the case that our research methods can't measure what “works”?
{"title":"Recidivism Reduction Research","authors":"","doi":"10.4018/978-1-7998-1147-3.ch003","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch003","url":null,"abstract":"Since 2009, more than 840 Second Chance Act grant awards have been made to government and nonprofit agencies, and taxpayers have paid nearly 700 million dollars in Second Chance grants. Additionally, $154 million has been spent on probation and parole supervision agencies and staff through the Justice Reinvestment Initiative. Yet, our probation and parole population continue growing! Given the amount of money taxpayers have invested in programs, it seems nothing works. In the 20th century, it was assumed that the use of randomized and control-group research designs and complex statistical analysis and state-of-the-art computer software would be sufficient to find what “works.” But, we have not yet found what “works.” This chapter asks two questions: 1) Is it the case that “nothing works”? or 2) Is it the case that our research methods can't measure what “works”?","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"7 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126028507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch008
The Workforce Innovation and Opportunity Act (WIOA) establishes performance accountability indicators and performance reporting requirements to assess the effectiveness of local workforce development programs. The WIOA State Porta provides the number of clients served per state. In 2017, the District served roughly 15,000 under the WIOA, 8,000 through a state apprenticeship program and 3,000 through University of the District of Columbia Community College's Division of Workforce Development. From 2009-2017, the District was designated as an “at risk” employment agency by the federal Department of Labor. Such a designation indicated that the Department questioned the capability of the District's workforce programs to employ local residents. And, the Department of Labor's “at risk” designation also suggested CSOSA clients referred to the District's employment programs would have been unlikely to have received WIOA, TANF, or SNAP training in a timely fashion to meet CSOSA's client accountability contracts.
{"title":"Job Training Need vs. Capacity","authors":"","doi":"10.4018/978-1-7998-1147-3.ch008","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch008","url":null,"abstract":"The Workforce Innovation and Opportunity Act (WIOA) establishes performance accountability indicators and performance reporting requirements to assess the effectiveness of local workforce development programs. The WIOA State Porta provides the number of clients served per state. In 2017, the District served roughly 15,000 under the WIOA, 8,000 through a state apprenticeship program and 3,000 through University of the District of Columbia Community College's Division of Workforce Development. From 2009-2017, the District was designated as an “at risk” employment agency by the federal Department of Labor. Such a designation indicated that the Department questioned the capability of the District's workforce programs to employ local residents. And, the Department of Labor's “at risk” designation also suggested CSOSA clients referred to the District's employment programs would have been unlikely to have received WIOA, TANF, or SNAP training in a timely fashion to meet CSOSA's client accountability contracts.","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130456069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch011
We now have a $4 trillion federal budget. We can spend this budget to expand our prison complex consisting of 1,719 state prisons, 109 federal prisons, 1,772 juvenile correctional facilities, 3,163 local jails, 80 Indian country jails and military prisons and immigration detention facilities. Or, we can build-up our military-industrial complex (i.e., our $600 billion for national defense and an additional $255 billion for out foreign affairs), Department of Homeland Security, and State Department. Or, we can increase our $750.7 billion budget to implement social service grants to state and local governments, which combined are a set of “protective” factors for probation and parole clients.
{"title":"Probation and Parole Protective Factors","authors":"","doi":"10.4018/978-1-7998-1147-3.ch011","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch011","url":null,"abstract":"We now have a $4 trillion federal budget. We can spend this budget to expand our prison complex consisting of 1,719 state prisons, 109 federal prisons, 1,772 juvenile correctional facilities, 3,163 local jails, 80 Indian country jails and military prisons and immigration detention facilities. Or, we can build-up our military-industrial complex (i.e., our $600 billion for national defense and an additional $255 billion for out foreign affairs), Department of Homeland Security, and State Department. Or, we can increase our $750.7 billion budget to implement social service grants to state and local governments, which combined are a set of “protective” factors for probation and parole clients.","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117287184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch004
CSOSA is the agency that supervises residents on probation and parole in the District of Columbia. In 2017, it supervised 16,407 District residents. In that year, 90% of CSOSA's clients' technical violations came from drug use and drug testing violations, and these technical violations caused 10% of the agency's revocation to incarceration in 2017. CSOSA sanctions its clients by sending them to day reporting center and CSOSA performs home visits (an astounding 45,124 in 2017). Yet, to date, there is scant evidence showing any relationship between sanctions, or day reporting centers, or home visits and recidivism and public safety. It's true, there is no agreement among social scientists as to the number of studies needed to confirm that an intervention “works.” However, to date, none of the methods used by CSOSA have been shown to “work.”
{"title":"The Court Services and Offender Supervision Agency (CSOSA)","authors":"","doi":"10.4018/978-1-7998-1147-3.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch004","url":null,"abstract":"CSOSA is the agency that supervises residents on probation and parole in the District of Columbia. In 2017, it supervised 16,407 District residents. In that year, 90% of CSOSA's clients' technical violations came from drug use and drug testing violations, and these technical violations caused 10% of the agency's revocation to incarceration in 2017. CSOSA sanctions its clients by sending them to day reporting center and CSOSA performs home visits (an astounding 45,124 in 2017). Yet, to date, there is scant evidence showing any relationship between sanctions, or day reporting centers, or home visits and recidivism and public safety. It's true, there is no agreement among social scientists as to the number of studies needed to confirm that an intervention “works.” However, to date, none of the methods used by CSOSA have been shown to “work.”","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115333181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch009
In 2018, the local District of Columbia auditor found that a section of the Department of Behavioral Health that performed psychiatric evaluations had significant staff turnover and long-standing position vacancies and that there had been a several-week period when approximately one-fourth of the Division's full-time positions were vacant. As a result, the Department's psychiatric evaluation waitlist grew, delaying many defendants' evaluations beyond the statutorily permissible timeframe. When the problem persisted, DC Superior Court judges threatened contempt citations. Moreover, the Department relied on a network of small to mid-sized nonprofit agencies to provide the vast majority of public behavioral health services. However, many of these nonprofits had experienced lengthy delays in reimbursement stemming from the Department of Behavioral Health's billing software, and some were forced to close. These circumstances suggested the CSOSA clients would have been unlikely to have received mental health treatment.
{"title":"Mental Health and Drug Treatment Need vs. Capacity","authors":"","doi":"10.4018/978-1-7998-1147-3.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch009","url":null,"abstract":"In 2018, the local District of Columbia auditor found that a section of the Department of Behavioral Health that performed psychiatric evaluations had significant staff turnover and long-standing position vacancies and that there had been a several-week period when approximately one-fourth of the Division's full-time positions were vacant. As a result, the Department's psychiatric evaluation waitlist grew, delaying many defendants' evaluations beyond the statutorily permissible timeframe. When the problem persisted, DC Superior Court judges threatened contempt citations. Moreover, the Department relied on a network of small to mid-sized nonprofit agencies to provide the vast majority of public behavioral health services. However, many of these nonprofits had experienced lengthy delays in reimbursement stemming from the Department of Behavioral Health's billing software, and some were forced to close. These circumstances suggested the CSOSA clients would have been unlikely to have received mental health treatment.","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129422477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1147-3.ch010
Burnham and Anderson observed that while a model can never be “truth,” a model might be ranked on a continuum from very useful, to useful, to somewhat useful, to essentially useless. The prevailing Risk-Needs-Responsivity (RNR) model is essentially useless for two reasons: 1) there is no difference in recidivism between Second Chance Grant recipients and non-Second Chance recipients, and 2) our probation and parole numbers have been increasing not decreasing as jurisdictions, unquestioningly, adhere to the RNR model's principles and tenets. The fundamental attribution bias of overestimating the role of person-factors while underestimating the role of each jurisdictional environment is a key aspect of RNR risk assessment algorithms. Thus, the RNR model and its associated risk assessment instruments have no ecological validity. More specifically, neither attends to variations in “Get Tough” jurisdictions policy. Yet, “Get Tough” variables are unacknowledged moderator variables.
{"title":"Missing “Get Tough” Risk Factors","authors":"","doi":"10.4018/978-1-7998-1147-3.ch010","DOIUrl":"https://doi.org/10.4018/978-1-7998-1147-3.ch010","url":null,"abstract":"Burnham and Anderson observed that while a model can never be “truth,” a model might be ranked on a continuum from very useful, to useful, to somewhat useful, to essentially useless. The prevailing Risk-Needs-Responsivity (RNR) model is essentially useless for two reasons: 1) there is no difference in recidivism between Second Chance Grant recipients and non-Second Chance recipients, and 2) our probation and parole numbers have been increasing not decreasing as jurisdictions, unquestioningly, adhere to the RNR model's principles and tenets. The fundamental attribution bias of overestimating the role of person-factors while underestimating the role of each jurisdictional environment is a key aspect of RNR risk assessment algorithms. Thus, the RNR model and its associated risk assessment instruments have no ecological validity. More specifically, neither attends to variations in “Get Tough” jurisdictions policy. Yet, “Get Tough” variables are unacknowledged moderator variables.","PeriodicalId":147452,"journal":{"name":"Community Risk and Protective Factors for Probation and Parole Risk Assessment Tools","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126491784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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