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Week 4 Discussion 1- Special Populations: A Challenge to Juvenile Justice

Open Posted By: ahmad8858 Date: 19/02/2021 High School Proofreading & Editing

Special Populations: A Challenge to Juvenile Justice [WLOs: 1, 2, 3] [CLOs: 1, 2, 3, 4, 5, 6]

Consider the goals of the juvenile justice system, which focus on reintegrating juveniles into the community as productive members of society. Prior to beginning work on this discussion, read Chapters 8 and 9 of Introduction to Juvenile Justice. (PROVIDED IN ATTACHMENTS) In addition,

ALL ARTICLES PROVIDED IN ATTACHMENTS!!!MUST USE AS RESOURCES!!

You are also encouraged to review the Week 4 Recommended Resources.

Compare and contrast treatment options for special populations identified in our text (i.e., early starters, juvenile gangs, or juvenile sex offenders) and advocate for, or against, shifting juveniles in this category to treatment options outside normal juvenile delinquency programs. You should identify a specific category identified as being part of special populations and a treatment option as part of the discussion. What are the benefits to this program in addressing the special population? Are there drawbacks, if so what are they? How are outcomes identified and measured?

Note: this discussion format will differ from formats in prior courses. The goal of this discussion forum is to have a single conversation about the topic of treatment for special populations of juvenile offenders, not a series of separate conversations.

Category: Accounting & Finance Subjects: Behavioral Finance Deadline: 12 Hours Budget: $150 - $300 Pages: 3-6 Pages (Medium Assignment)

Attachment 1

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8Residential and Institutional Placement of

Juveniles

Comstock/Thinkstock

Learning Objectives

After studying this chapter, you should be able to accomplish the following objectives:

Summarize the history behind the residential

placement of youth.

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During 2008, a juvenile correctional center in Ohio lost over half of its staff. The center, called Marion Juvenile Correctional Facility, saw a significant increase in violence among residents of the facility. In fact, according to the Columbus Dispatch,

Assaults on staff members have resulted in a broken nose, a slash across the face, choking, unconsciousness, bites, a blown-out knee and the indignity of being doused with milk cartons filled with urine. Guards, teachers and other prison workers regularly are assaulted. Last year, they missed the equivalent of seven years of workdays because of injuries and disabilities. Large youth fights have sent staff members to the hospital four, five, six at a time. Slightly more than half of the frustrated, frightened and fatigued guards quit last year, some walking away from $15.80-an-hour jobs after only a few days. (Ludlow, 2008)

As with any situation, the causes of violence are varied; however, reports indicated that gang violence and understaffing all contributed to the situation at the Marion Juvenile Correctional Facility. The state was hit with a federal lawsuit after evidence of widespread abuse by staff surfaced. As a result, correctional staff members were trained to use less force when managing unruly youth. However, as noted by the unions representing correctional officers, the hands-off policy created concerns for correctional officers, who indicated they felt unsafe at the facility.

At the time the Columbus Dispatch article was written in early 2008, the department director expressed optimism about being able to turn around the correctional facility. The director noted that staff training would help to reduce use-of-force incidents against youth. In addition, the facility worked to identify gang-involved youth and

Define confinement and who is most likely to be

sentenced to institutions for juveniles.

Explain the different types of short-term residential

placements for youth.

Describe the advantages and disadvantages of group

homes.

Explain the degree of effectiveness of wilderness

camps.

Identify the different types of short-term residential

placements for youth.

Summarize the issues associated with long-term

secure correctional facilities.

Describe the risks involved with confining juveniles

in adult facilities.

Identify the components of successfully helping

juveniles reintegrate into society after release.

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transfer them to other facilities. Just one year later, on January 8, 2009, the Ohio Department of Youth Services issued a press release announcing the closure of the Marion Juvenile Correctional Facility.

Fast forward to today. Ohio has made great strides to reform its juvenile justice system. Since 2008, the juvenile justice population residing in youth centers has declined from 1,700 youth to 429. The state also created the Reentry Continuum, an innovative plan that relies on best practices in rehabilitation. The plan calls for a number of principles that guide Ohio's approach toward managing youth in the juvenile justice system:

Adopt the Effective Practices in Community Supervision (EPICS) model for parole staff Implement risk and need assessment tools to assign treatment programming Reduce the length of time on parole for low and low-moderate risk youth by collaborating with judges Support reentry courts at the county level Develop discharge plans to assist youth with any needed services post-release

The Reentry Continuum is just one example of major reforms that states nationwide have adopted to reduce the number of youth in custody.

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8.1 Introduction

Confining juveniles as a form of punishment is not without controversy. Throughout this text, we have discussed how shifts in policy are often influenced by the social climate of the time. Not surprisingly, when it comes to confining juvenile offenders as a form of punishment, we have seen (and continue to see) shifts in policy. For example, there was an increase in the use of confinement for juveniles during the get-tough era of the 1980s and 1990s. Since that time, however, states have reduced by nearly half the population of youth confined. The recent shift is due to several factors. For one thing, the cost of confinement has forced states to rethink their policies. Moreover, there is a growing recognition that confinement can exacerbate rather than solve the problems that bring youth to the juvenile justice system. Even so, the confinement of juveniles has a long history and is unlikely to be abandoned in the near future.

The use of confinement has often been justified on the grounds of deterrence. For example, although probation is the most widely used sanction for juveniles, there has always been a concern that the general public views probation as merely a slap on the wrist. From a deterrence standpoint, justice should be swift, certain, and just severe enough to outweigh the benefits of crime. Using the biblical reference "to spare the rod is to spoil the child," some observers argued during the 1990s and early 2000s that only after delinquents experienced the harsh hand of justice in the form of boot camps, chain gangs, or confinement would they think twice about committing crime in the future. Policymakers argued that the firm hand of justice would steer youth onto the right path.

Over the past decade, there has been a groundswell of support for reducing the use of confinement for juveniles. For example, it has been argued that institutions for juveniles act as "crime schools," as youth from various criminal backgrounds come together and reinforce their criminal status. In these situations, juveniles can learn how to commit other crimes from fellow juveniles. Second, there are concerns about the physical and emotional effects of confinement on youth who are still developing and growing. In particular, youth could be traumatized by their confinement experiences. Third, there are concerns regarding inequality in terms of who is placed in these settings. In particular, girls appear to be more likely to be sent to facilities for minor charges, and African American youth are disproportionately represented. Finally, critics contend that juveniles sentenced to serve time in adult facilities do worse than those who remain in the juvenile system. The complexity of these issues cannot be underestimated. We will discuss these and other issues in this chapter as we examine the impact and effectiveness of institutional placement for juveniles.

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Out-of-home placements for juveniles range from detention centers to group homes to residential treatment centers.

Comstock/Thinkstock

8.2 Defining Confinement for Juveniles

The words confinement or institutional placement often conjure an image of a large, concrete prison with bars and barbed wire. These images of prison have been popularized by movies such as Shawshank Redemption and Dog Pound, and television shows such as Orange Is the New Black and Empire. Although media- derived images of prison may be accurate for some maximum-security adult prisons, juvenile facilities are more varied and complex. The terminology used to define juvenile facilities varies so greatly that the terms residential or out-out- of- of- homehome placements placements are often used rather than the term prison. In fact, according to Melissa Sickmund (2010),

Juvenile facilities are known by many different names across the country: detention centers, juvenile halls, shelters, reception and diagnostic centers, group homes, wilderness camps, ranches, farms, youth development centers, residential treatment centers, training or reform schools, and juvenile correctional institutions. (p. 1)

The lack of a standard definition for these facilities can lead to a great deal of confusion. For example, to examine whether residential placement or community placement is more effective in reducing recidivism among youth, we would need to make sure we are not comparing apples to oranges. We would also need to decide how to measure or quantify residential placement. We would expect, for example, that a juvenile placed in a wilderness camp would be exposed to a different set of experiences than a juvenile placed in a secure correctional facility. In an effort to identify these differences and the impact they have on the behavior of juveniles, we will examine each of these settings in detail in subsequent sections. First, though, let's look at the broad data on which and how many juveniles are in these facilities, with the understanding that the common thread among all of these facilities is that juveniles reside at the facility rather than in their homes.

Population Characteristics of Residential Facilities

The Office of Juvenile Justice and Delinquency Prevention (OJJDP) conducts a census of residential facilities for juveniles every other year. The results of the 2016 survey indicated that 45,567 juvenile offenders were held in juvenile residential facilities, representing a decline of more than 58% since 2000 (Puzzanchera, Hockenberry, Sladky, & Kang, 2018). Table 8.1 illustrates that the majority of juvenile facilities are labeled "residential treatment centers." Those facilities most similar to what we consider a "prison" in adult terms are labeled "long- term secure correctional facilities." Table 8.1 indicates that there are 189 of these facilities across the country.

Table 8.1: The number of residential juvenile facilities by type, 2016

Detention center Shelter

Reception/diagnostic center

Group home

Ranch/wilderness camp

Long- term

secure

Residential treatment

center

Number of

662 131 58 344 30 189 678

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facilities Source: From "Table: Year by facility self-classification, United States," in Juvenile residential facility census databook: 2000–2016, by C. Puzzanchera, S. Hockenberry, T. J. Sladky, and W. Kang, 2018, Retrieved from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / s e l e c t i o n _ p ro fil e . a s ph t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / s e l e c t i o n _ p ro fil e . a s p ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / s e l e c t i o n _ p ro fil e . a s p )( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / s e l e c t i o n _ p ro fil e . a s p )

By examining the latest trends, we see in Figure 8.1 that the number of juveniles in residential placement has declined significantly. This decline is not surprising, since as we discussed in Chapter 1 the overall arrest rates among youth have also declined significantly.

Figure 8.1: Juveniles in residential placement, 2000 and 2014

From "Table: Number of facilities and juvenile offenders by facility size, United States (for years 2004 and 2014)," in Juvenile residential facility census databook: 2000–2016, by C. Puzzanchera, S. Hockenberry, T. J. Sladky, and W. Kang, 2018, Retrieved from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / d i s p l a y _ p ro fil e . a s ph t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / d i s p l a y _ p ro fil e . a s p ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / d i s p l a y _ p ro fil e . a s p )( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / d i s p l a y _ p ro fil e . a s p )

As seen in Table 8.2, the number of juveniles in residential placement varies quite a bit by state. For example, Table 8.2 lists both the number of juveniles in placement (for 2015) and the rate of placement. The rate of placement is the number of juveniles in custody per 100,000 youth. A rate helps to account for differences in state population. In other words, we would expect that California would have more juveniles in custody, given that it is the most populous state in the country. However, in the case of California, we see the placement rate of 165 is below that for many other states. Six of the most populous states—California, Texas, Florida, New York, Pennsylvania, and Ohio—have reduced their placement rates by nearly half since 1997 (Hockenberry, 2018).

Table 8.2: The number of juveniles in residential placement by state, 2015

State where offense occurred (upper age of juvenile court jurisdiction in 2015)

Number of juvenile offenders in public or private residential placement, 2015

Residential placement rate, 2015 (per 100,000 youth)

U.S. total 48,043 152

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Alabama (17) 849 168

Alaska (17) 207 262

Arizona (17) 717 98

Arkansas (17) 555 175

California (17) 6,726 165

Colorado (17) 999 177

Connecticut (17) 141 38

Delaware (17) 162 176

District of Columbia (17) 105 251

Florida (17) 2,853 153

Georgia (16) 1,110 111

Hawaii (17) 51 39

Idaho (17) 393 200

Illinois (17) 1,542 112

Indiana (17) 1,563 217

Iowa (17) 675 207

Kansas (17) 564 177

Kentucky (17) 510 112

Louisiana (16) 831 193

Maine (17) 81 67

Maryland (17) 612 101

Massachusetts (17) 426 66

Michigan (16) 1,554 172

Minnesota (17) 852 149

Mississippi (17) 243 74

Missouri (16) 948 173

Montana (17) 171 170

Nebraska (17) 465 225

Nevada (17) 627 209

New Hampshire (17) 69 54

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New Jersey (17) 636 69

New Mexico (17) 363 164

New York (15) 1,386 99

North Carolina (15) 468 60

North Dakota (17) 144 203

Ohio (17) 2,163 178

Oklahoma (17) 552 131

Oregon (17) 1,113 286

Pennsylvania (17) 2,826 228

Rhode Island (17) 198 200

South Carolina (16) 693 161

South Dakota (17) 228 254

Tennessee (17) 660 97

Texas (16) 4,299 153

Utah (17) 453 114

Vermont (17) 27 47

Virginia (17) 1,227 147

Washington (17) 921 130

West Virginia (17) 567 329

Wisconsin (16) 762 147

Wyoming (17) 177 296 Source: From "Table: In 2015, the national commitment rate was twice the detention rate, but rates varied by state," in Juveniles in residential placement, 2015, by S. Hockenberry, 2018, Retrieved from h t t p s : / / w w w. o j j d p . g o v / p u b s / 2 5 0 9 5 1 . p d fh t t p s : / / w w w. o j j d p . g o v / p u b s / 2 5 0 9 5 1 . p d f ( h t t p s : / / w w w. o j j d p . g o v / p u b s / 2 5 0 9 5 1 . p d f ) ( h t t p s : / / w w w. o j j d p . g o v / p u b s / 2 5 0 9 5 1 . p d f )

The types of offenses that lead to residential placement are shown in Figure 8.2. Person offenses, which include violent offenses such as murder and robbery, represent the largest category, with the second largest category being property offenses. In fact, 60% of the juveniles in residential placement were there as a result of a person or property offense.

Figure 8.2: Percentage of juveniles in any residential setting by offense type, 2015

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From "Table: Year of census by most serious offense general," in Easy access to the census of juveniles in residential placement: 1997–2015, by M. Sickmund, T. J. Sladky, W. Kang, and C. Puzzanchera, 2017, Retrieved from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s ph t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p )( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p )

If we examine gender, we can see in Figure 8.3 that 85% of youth in residential placement are boys. What this doesn't illustrate, however, is that girls of color are more likely than white girls to be placed in a residential setting. Girls are also more likely to be placed in residential settings for lower level offense. According to the latest statistics available from the OJJDP (Sickmund, Sladky, Kang, & Puzzanchera, 2017), more than half of youth placed in residential settings for running away are girls.

Figure 8.3: Percentage of juveniles in residential placement by gender, 2015

Eighty-five percent of the youth in residential placement were boys.

From "Table: Year of census by sex," in Easy access to the census of juveniles in residential placement: 1997–2015, by M. Sickmund, T. J. Sladky, W. Kang, and C. Puzzanchera,

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2017, Retrieved from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l ah t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ?y. a s p ? ro w _ v a r = v 0 1 & c o l _ v a r = v 0 2 & d i s p l a y _ t y p e = ro w p &ro w _ v a r = v 0 1 & c o l _ v a r = v 0 2 & d i s p l a y _ t y p e = ro w p & e x p o r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0e x p o r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ?( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ? ro w _ v a r = v 0 1 & c o l _ v a r = v 0 2 & d i s p l a y _ t y p e = ro w p & e x p o r t _ fro w _ v a r = v 0 1 & c o l _ v a r = v 0 2 & d i s p l a y _ t y p e = ro w p & e x p o r t _ f i l e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 )i l e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 )

If we examine race, we see that there are differences overall and by state. Table 8.3 illustrates that the total percentage of minority youth in custody in the United States is higher (42% black, 22% Hispanic) than for white youth (31%). The table also illustrates differences by state. Table 8.3 shows 17 states where 50% or more of the population under state custody is black. The jurisdictions with the highest rates include District of Columbia (97%), Delaware (80%), Louisiana (80%), Maryland (79%), Mississippi (77%), Georgia (74%), and New Jersey (72%). What is difficult to assess from the table is the extent to which these percentages represent disproportionality.

Table 8.3: Percentage under state custody by race/ethnicity, 2015

State of offense White Black Hispanic1 American Indian2 Asian Other

U.S. total 31% 42% 22% 2% 1% 2%

Alabama 35 60 3 0 0 1

Alaska 38 14 1 36 1 10

Arizona 33 16 36 8 1 7

Arkansas 36 57 6 0 1 1

California 13 28 55 1 2 1

Colorado 36 21 39 1 1 1

Connecticut 23 47 26 0 0 4

Delaware 13 80 7 0 0 2

Dist. of Columbia 0 97 0 0 0 0

Florida 29 62 9 0 0 0

Georgia 18 74 5 0 1 2

Hawaii 18 0 6 0 53 29

Idaho 70 2 23 2 2 1

Illinois 21 63 14 0 0 1

Indiana 53 36 7 0 0 4

Iowa 56 29 9 2 1 2

Kansas 46 33 19 1 1 1

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Kentucky 56 34 2 0 0 8

Louisiana 17 80 1 1 0 1

Maine 78 15 0 4 0 4

Maryland 14 79 6 0 0 0

Massachusetts 23 30 41 0 1 6

Michigan 40 47 6 1 0 6

Minnesota 38 40 7 10 2 4

Mississippi 22 77 0 0 0 1

Missouri 49 44 3 0 0 3

Montana 54 12 12 16 0 5

Nebraska 40 25 23 5 1 5

Nevada 25 37 31 2 2 3

New Hampshire 78 9 9 4 0 4

New Jersey 8 72 18 0 0 0

New Mexico 14 7 74 4 0 2

New York 28 52 16 1 1 2

North Carolina 21 67 7 2 0 3

North Dakota 54 13 4 25 0 4

Ohio 42 50 3 0 0 4

Oklahoma 39 40 8 11 0 2

Oregon 56 13 24 4 1 1

Pennsylvania 29 53 14 0 0 3

Rhode Island 32 30 32 0 3 3

South Carolina 32 48 16 1 0 3

South Dakota 49 4 3 39 1 3

Tennessee 46 41 9 0 0 3

Texas 21 34 44 0 0 1

Utah 50 9 34 5 2 1

Vermont 89 11 0 0 0 0

Virginia 24 62 11 0 0 3

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Washington 43 22 20 6 2 7

West Virginia 84 8 2 1 0 5

Wisconsin 28 56 9 3 1 2

Wyoming 66 7 14 12 0 2 1The Hispanic category includes person of Latin American or other Spanish culture or origin regardless of race. 2American Indian includes Alaskan Natives; Asian includes Pacific Islanders. Source: From "Table: Race/ethnicity by state, 2015," in Easy access to the census of juveniles in residential placement: 1997–2015, by M. Sickmund, T. J. Sladky, W. Kang, and C. Puzzanchera, 2017, Retrieved from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / S t a t e _ R a c e . a s p ?h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / S t a t e _ R a c e . a s p ? s t a t e = & t o p i c = S t a t e _ R a c e & y e a r = 2 0 1 5 & p e rc e n t = ro ws t a t e = & t o p i c = S t a t e _ R a c e & y e a r = 2 0 1 5 & p e rc e n t = ro w ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / S t a t e _ R a c e . a s p ? ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / S t a t e _ R a c e . a s p ? s t a t e = & t o p i c = S t a t e _ R a c e & y e a r = 2 0 1 5 & p e rc e n t = ro w )s t a t e = & t o p i c = S t a t e _ R a c e & y e a r = 2 0 1 5 & p e rc e n t = ro w )

Disproportionate Minority Contact (DMC)

The rate of confinement for minority populations has led to a number of initiatives, most notably the Disproportionate Minority Contact (DMC) initiative designed to reduce the number of minorities who come in contact with the system. According to the Juvenile Justice and Delinquency Prevention Act of 2002, states receiving formula grants are required to address the issue of overrepresentation of minorities at each stage of the juvenile justice system, which includes institutions. The OJJDP has become a leader in collecting data to examine the national rates of contact. As an example of this leadership, they developed the National Disproportionate Minority Contact Databook (see https://www.ojjdp.gov/ojstatbb/dmcdb/ (https://www.ojjdp.gov/ojstatbb/dmcdb/) ).

Data from this source are referred to as the Relative Rate Index (RRI). The RRI assesses the levels of disproportionate minority contact at various stages of juvenile justice system processing at the national level. This rate helps us understand the extent of disproportionality by taking into account the population size of different minority groups (e.g., black, Hispanic, Asian, American Indian) in the United States. The rate calculated is compared to the rate for white youth. The OJJDP created the RRI matrix to help states and jurisdictions measure levels of disparity within different parts of the juvenile justice system. By capturing the extent of disproportionate minority contact within communities, stakeholders can identify decision points that may need policy reforms. These data now allow us to examine trends over time.

Figure 8.4 illustrates that, with the exception of Asian American youth, all other minority youth have a rate of placement in residential settings that is higher than for white youth. Black and Hispanic youth have the highest rates of placement compared to other groups.

Figure 8.4: Relative rate index for youth receiving residential placement, 2005–2015

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*AHPI: Asian, Hawaiian, or Pacific Islander

**AIAN: American Indian or Alaskan Native

From "Relative rate indices of adjudication and placement of delinquency referrals," in National disproportionate minority contact databook, by C. Puzzanchera and S. Hockenberry, 2018, Retrieved from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / d m c d b / a s p / d i s p l a y _ t re n d . a s p ?h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / d m c d b / a s p / d i s p l a y _ t re n d . a s p ? d i s p l a y _ i n = 1 & p o i n t = 9 & o f f e n s e = 1 & d i s p l a y t y p e = r r i & s h o w _ c h a r t = y e sd i s p l a y _ i n = 1 & p o i n t = 9 & o f f e n s e = 1 & d i s p l a y t y p e = r r i & s h o w _ c h a r t = y e s ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / d m c d b / a s p / d i s p l a y _ t re n d . a s p ?( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / d m c d b / a s p / d i s p l a y _ t re n d . a s p ? d i s p l a y _ i n = 1 & p o i n t = 9 & o f f e n s e = 1 & d i s p l a y t y p e = r r i & s h o w _ c h a r t = y e s )d i s p l a y _ i n = 1 & p o i n t = 9 & o f f e n s e = 1 & d i s p l a y t y p e = r r i & s h o w _ c h a r t = y e s )

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8.3 Short-Term Residential Facilities

Several different types of facilities are referred to as short-term residential facilities, including detention centers, reception/diagnostic centers, and youth shelters. Detention centers provide a temporary form of confinement typically used before the intake or adjudication phase. The police may decide to detain youth who pose a risk to themselves or others. In addition, if the police are unable to locate a youth's parents or guardians, they may place the juvenile in detention until the responsible party can be located.

Reception/diagnostic centers typically house youth for short periods while correctional officials assess the juveniles' needs in order to determine the best placement. The process is similar to the intake process; however, two characteristics distinguish it from the traditional intake process. First, unlike the intake process in which a youth may meet with a probation officer in the community, youth remain confined during this assessment process. Second, the assessment of youth at this stage often occurs once the youth has been adjudicated as delinquent and has been remanded to serve time in a residential facility (Sickmund, 2010). For example, in Ohio, all youth committed to the Department of Youth Services are sent to one reception center to be assessed for placement in one of the state's secure juvenile correctional facilities.

Youth shelters are another example of a short-term residential facility. Shelters are designed to provide short- term placement for youth who cannot be immediately returned to their families. Although designed primarily to serve status offenders and abuse and neglect cases, youth shelter care facilities can also serve delinquent youth if a detention center bed is unavailable. Most youth spend only days at youth shelters; however, the stay can be extended to weeks if the court finds placement to be difficult. Some youth shelters provide extensive services (e.g., psychological counseling, educational services), whereas others simply provide temporary supervised housing (Hicks-Coolick, Burnside-Eaton, & Peters, 2003).

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8.4 Group Homes

Group homes may be either short or long term, and they can serve a variety of youth in the juvenile …

Attachment 2

Research Review: Independent living programmes: the influence on youth ageing out of care (YAO)

Anna Yelick Lecturer, College of Social Work, Florida State University, Tallahassee, FL, USA

Correspondence: Anna Yelick, College of Social Work, Florida State University, 296 Champions Way, University Center Building C, Tallahassee, FL 32306, USA E-mail: [email protected]

Keywords: educational attainment, employment, independent living programmes, life skills, productive outcomes, youth ageing out of care

Accepted for publication: December 2014

A B S T R AC T

Independent living programmes (ILPs) aid in promoting productive outcomes for youth ageing out of care (YAO). This narrative review aimed to determine if sufficient evidence exists to substantiate state- ments regarding the effectiveness of ILPs based upon outcome studies published from January 2006 through December 2012. Are current ILPs effectively promoting independent living and productive out- comes among youth leaving foster care, relative to similar youth who do not participate in an ILP? Six studies published in English, in the USA and in peer-reviewed journals included non-experimental design (n = 1), quantitative designs (n = 2), mixed methods design (n = 2) and randomized design (n = 1). Five outcomes addressing education, employment, housing, mental health, and living skills emerged. Weak evidence that ILPs effectively aid YAO exists. Additionally, inconsist- encies exist in methodology. Finally, differences in important compo- nents in the ILPs exist, making comparisons difficult.

I N T R O D U C T I O N

Approximately 10% (23 396) of youth emancipated from the care system in 2012 (Adoption and Foster Care Analysis and Reporting System (AFCARS) 2011). These emancipated youth (youth who have aged out of the system, usually when they reach 18 years old) typically face disadvantages in terms of educational attainment and employment outcomes compared to non-fostered youth (Unrau et al. 2011). For example, 50% of foster youth obtain a high school diploma or general educational development (GED) degree compared to nearly 70% of non-fostered youth (Sheehy et al. 2001; Wolanin 2005; Unrau et al. 2011). Post-secondary education is not encouraged among the foster youth population as approximately 15% of foster youth enroll in college-preparatory classes compared to 32% of non-fostered youth (Unrau et al. 2011). Further, while nearly 79% of foster youth express an interest in attending a post- secondary education programme (Courtney et al. 2010), as few as 7–13% enroll in post-secondary edu- cation programmes (Casey Family Programs 2010) and fewer than 6% obtain post-secondary degrees (Pecora et al. 2010).

Youth ageing out of care (YAO) also face challenges in terms of housing and life skills. YAO are independ- ent, and as such, must find housing, pay bills and find employment shortly after leaving care.YAO often lack social and familial support, which arguably leads to a lack in life skills and resources to successful independ- ent living (Lemon et al. 2005; Montgomery et al. 2006; Collins et al. 2008; Avery & Freundlich 2009; Avery 2010; Harder et al. 2011). Additionally, YAO tend to live independently at an earlier age compared to non-fostered youth – many of whom return home or have financial and emotional support well into their 20s (Lemon et al. 2005; Montgomery et al. 2006).

Independent living programmes (ILPs) were estab- lished to aid YAO obtain productive positive out- comes, such as educational attainment, employment stability, housing and life skills (Montgomery et al. 2006; Petr 2006; Naccarato & DeLorenzo 2008; Uzoebo et al. 2008; Mares 2010; Kroner & Mares 2011; Mares & Kroner 2011; Powers et al. 2012). The Independent Living Initiative established ILPs, which was an amendment to the Social Security Act (Mares & Kroner 2011), and established aid for foster youth to live independently, as well as enabled states to develop life skills, academic achievement and

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vocational training programmes to circumvent home- lessness, dependence on public assistance and institu- tionalization after emancipation (Hardin 1987). ILPs were further develop with the enactment of the John Chafee Foster Care Independence Program of 1999 and the Foster Care Independence Act of 1999, which provided states with additional funding in response to independent living research findings and child welfare advocates calling for an amendment of the Social Security Act (Allen & Bissell 2004). The Fostering Connections to Success and Increasing Adoptions Act of 2008 improved outcomes for children in foster care by expanding the definition of child. This definition included youth ages 18–20, enrolled in secondary education, post-secondary education or vocational training programmes; employed 80 hours a month or more; or who are incapable of attending school or work because of a medical condition (Mares & Kroner 2011).

Approximately two-thirds of eligible youth receive independent living services (Courtney 2005; Avery 2010), indicating that ILPs are widely used by youth exiting the foster care system. ILPs aim to promote skills for independent living (Reilly 2003; Lemon et al. 2005; Montgomery et al. 2006; Geenen & Powers 2007), encouraging youth to become productive members of society and attain positive productive out- comes (independent living, education, employment and increased life skills) despite lacks in social and familial support (Montgomery et al. 2006; Mares & Kroner 2011).

Effectiveness of ILPs

Two systematic reviews have been completed (Montgomery et al. 2006; Naccarato & DeLorenzo 2008) assessing the efficacy of ILPs in accomplishing the projected aims discussed earlier.The Montgomery et al. (2006) review aimed to examine whether ILPs are effective at providing youth with skills that enhance their transition to independence.They included studies conducted prior to January 2006, which examined educational attainment, employment, housing, health and life skills for youth leaving the care system. They excluded any study that examined programmes specifi- cally designed for special populations (i.e. special needs, teen parenting, juvenile justice concerns).

The authors suggest evidence indicating that ILPs improved educational, employment and housing out- comes for YAO; however, the evidence was weakened by evaluation methodology – specifically, a lack of randomized control trials (RCTs), as non-randomized

studies are susceptible to bias. Confidence regarding the effectiveness of the ILP was low because of the inability to say with certainty that observed differences are attributable solely to the ILP. Despite this limita- tion, approximately 55% of the ILP group graduated from high school. However, discrepancies between YAO and the general population youth still exist. Approximately 86% of the general population youth graduated from high school, according to the US National Center for Education Statistics (Greene 2002). In addition, the national rate for employment differs between YAO and the general population youth, indicating that ILPs have yet to bridge the gap in positive productive outcomes for YAO compared to the general population youth.

The second systematic review by Naccarato & DeLorenzo (2008) aimed to examine studies regard- ing the effectiveness of ILPs in youth transitioning out of the care system from 1990 until 2006 in and outside the USA. However, the review only included studies in the USA and UK. The authors reviewed 19 articles, which met the four criteria: (i) the ILP aimed to increase readiness for youth leaving the care system; (ii) reported on education, employment, housing and mental health; (iii) published in a peer-reviewed journal and in English; and (iv) discussed transitional services.

The authors suggested that the studies they reviewed offered recommendations regarding improv- ing services to YAO. Some of the recommendations were to improve ILP practice, policy and research. The authors also suggested that the studies varied greatly in measurement of the ILP, specifically, in sample size, demographics, placement histories, support networks and outcome measures.The authors recommended a national database with input from researchers and practitioners in the field in order to design a functional information system. Non-uniform measurements make it difficult to determine the effec- tiveness of ILPs, indicating service goals and quality often vary among different ILPs (Courtney 2005; Avery 2010), making it difficult to formulate general assessments of ILPs and the effectiveness in aiding YAO.

Purpose of paper

This paper aims to examine newer literature published from January 2006 through December 2012 to deter- mine if sufficient evidence exists to substantiate state- ments made that ILPs effectively promote productive outcomes (i.e. educational attainment, employment,

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housing, mental health and life skills) for youth leaving the care system. The paper includes only studies conducted and published within the USA, in English and in peer-reviewed journals.

Research question

Are current ILPs effectively promoting independent living and productive outcomes among youth ageing out of the care system relative to similar youth who do not participate in an ILP?

M E T H O D S

Study selection

The present paper reviewed peer-reviewed studies published in the USA and in English. The review includes quasi-experimental and non-experimental group outcome studies. The review included several additional inclusionary and exclusionary criteria. The inclusionary criteria were as follows: (i) the study must contain information regarding an ILP; (ii) the study must have measureable outcomes (educational attainment, employment, housing, mental health/ special needs or life skills); and (iii) the study must examine ILPs for foster youth or residential care youth, or youth ageing out of the care system (YAO) only. The exclusionary criteria were (i) if the study did not examine ILPs; (ii) if the study was published prior to January 2006; or (iii) if the study was conducted outside the USA.

The electronic databases searched included the Cochrane Central Register of Controlled Trials, The Campbell Library, PsycINFO, Sociological Abstracts,

Applied Social Sciences Index and Abstracts (ASSIA) and Web of Science. The keyword search terms were (i) ab(Foster Care Youth) AND ab(Independent Living); (ii) ab(Foster Youth) AND ab(Independent Living Programs); (iii) ab(Independent Living) AND ab(Evaluation) AND ab(Randomized Control Trials); and (iv) ab(Foster Care) AND ab(Foster Care Youth) AND ab([Independent Living Programs OR ILPs]) AND ab(Outcomes).This resulted in an initial pool of 135 citations. Of these, 32 abstracts were examined, resulting in the inclusion of six primary studies (refer to Table 1 and Fig. 1).

Search results

The six primary studies utilized qualitative methods (one study), mix methods (two studies) and quantita- tive methods (three studies) to assess the effectiveness of ILPs from across the USA (Table 2 provides a summary of each study). The study participants were foster youth aged 16 and older preparing to leave the care system, or in the case of the qualitative study – the participants could include service providers. Out- comes of interest include secondary education, post-secondary education, employment, housing attainment, mental health or other special needs, and achieving life skills.

The study by Petr (2006) utilized a qualitative approach to evaluate the Kansas Independent Living Program in addition to five private contract agencies to assess youths’ perspectives (n = 27) regarding the quantity and quality of independent living services. This study utilized a convenience sample and included two groups: youth still in custody (n = 19) and youth out of custody (n = 8). Only youth aged 16 and up were included in the study (mean age of 17.3).

Table 1 Database search

Databases search

Database Date Results

Cochrane Library Since 2000 0 Campbell Library Since 2000 1 – Systematic review completed in 2006 –

restricted review to after 2006 Web of Science January 2006 through December 2012 51 PsycINFO January 2006 through December 2012 78 – These four databases were searched

simultaneously in order to reduce duplications. Another review was discovered; however, it also examined ILPs prior to January 2006

Sociological Abstracts January 2006 through December 2012 Applied Social Science Index

and Abstracts (ASSIA) January 2006 through December 2012

Social Services Abstracts January 2006 through December 2012

ILP, independent living programmes.

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Fifty-one per cent (n = 14) of the total sample were male (57% for in custody, 37.5% for out of custody). Approximately 51% (n = 14) of the total sample were Caucasian (42% for in custody and 87.5% for out of custody), while 33% (n = 9) of the total sample were African-American (47% for in custody). Of the eight participants in the out of custody group, six had a high school diploma (or GED equivalent) and two were in college. There were five outcomes assessed in this study: (i) education; (ii) mentors and support systems; (iii) life skills training; (iv) vocational preparation; and (v) knowledge of post-custody independent living benefits.The interviews were transcribed and analysed using Atlas ti, a qualitative software program. Petr coded units of the interview text according to the themes presented and then grouped the themes by commonality.

The study by Uzoebo et al. (2008) examined a spe- cific ILP, VISIONS, utilizing a mixed methods approach. It has been included in the review based upon the important perceptions discussed by youth receiving services. The quantitative data were col- lected using the Ansell Casey Life Skills Assessment (ACLSA). There were both pre-test and post-test assessments of the ACLSA for 89 participants. Quali- tative data were gathered using the Life Skills Evalu- ation Questionnaire for 24 participants. The average age of the participants was 16 years, with 63% female and 61% African-American. The average length of stay in the programme was 17 months. The outcomes of this study included determining perceptions of the

life skills received by the participants regarding the benefits of the programme, barriers to skills acquisi- tion and the role of the youth–mentor relationship in promoting skills development.

The study by Mares (2010) utilized a mixed methods approach using a focus group (n = 35) as well as administrative data from Lucas County in Ohio. The study included a sample of 108 youth who had emancipated from an ILP from 2005 through 2007. The information collected via the tracking data included demographic characteristics, clinical charac- teristics, foster home placements, outcomes at dis- charge and receipt of post-emancipation services. Five needs emerged during the focus groups: (i) higher amount for clothing vouchers; (ii) assistance obtaining a driver’s licence; (iii) provide home-based independ- ent living life skills training; (iv) ensure confidentiality of foster care placement packet; and (v) address the perception of unfair/unequal treatment by the foster parent(s) towards the foster youth. These themes included the expressed views of the participants and observations made by the research team. The modera- tor, a social work student, and the author discussed the observations during meetings. The transcriptions provided illustrative quotations for each theme iden- tified. In addition to the qualitative reports from the youth participants, surveys were collected from 83 public and private service providers using an online survey constructed by the author with input from the research team. The survey included 22 items contain- ing respondent information, programme information,

Potentially relevant studies identified and screened for retrieval (n=135)

Ineligible studies excluded based on title, language or date. In addition, studies using “grey” literature have also been excluded (n=103)

Abstracts of studies retrieved (n=32)

Studies excluded if not looking at independent living programmes/ transitions for foster youth (n=21)

Potentially appropriate studies for review; studies evaluated using the inclusion/ exclusion criteria worksheet (n=11)

Studies excluded from the review if there was no measurable outcome related to an independent living programme (n=5)

Primary studies with usable information by outcome (n=6)

Figure 1 Flowchart of the primary six studies included in this review.

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Table 2 Summary of the six primary studies included in the narrative review

Study Year Title Study population Study design Primary outcome

Kroner & Mares 2011 Living arrangements and level of care among clients discharged from a scattered-site housing-based independent living programme

Foster and former foster youth mean age 17.86

XO YO OXO

Housing outcomes: Three-fourths of youth were discharged to an independent level of care, 28% living by self, 13% living with friend, or 17% living with a relative. 55% of sample attained a status of independent living.

Mares & Kroner 2011 Lighthouse independent living programme: Predictors of client outcomes at discharge.

Foster and former foster youth mean age 17.9

OXO OYO

Educational outcome: They indicated that participants with mental-health problems were less likely to complete high school (0.60). In addition, participants who stayed in the programme longer were more likely to complete high school (0.96). Participants who were older at admission were also more likely to complete high school (1.55 and 2.35).

Employment outcome: Being 1 year older when entering into the programme predicated high rates of employment (1.55 to 2.35). Staying at least 1 month longer before exiting the programme also predicted high rates of paid employment (1.10). Participant without mental-health problems were also more likely to have paid employment (0.460).

Housing outcome: Participants who were older when entering the programme were more likely to live independently (1.55 and 2.35) at discharge. Participants who remained in the programme at least 1 month longer were also more likely to have independent housing (1.10). Participants who reported being parents also more likely to have independent housing at discharge (2.0).

Mental health/special needs outcome: Youth with mental-health problems are less likely to complete high school (0.61), find employment (0.64) or establish independent housing (0.68).

Uzoebo et al.

2008 Deconstructing youth transition to adulthood services: Lessons learned from the VISIONS programme

Foster and former foster youth mean age 16.0

OXO OYO

Life skills outcome: Participants reported higher mastery of skills in areas of daily living skills, work life, money management and budgeting, and self-care. At follow-up, participants demonstrated an increase in skills acquisition from 52% to 55%. Participants indicated receiving training in a class room setting was less efficacious to learning via a mentor or from ‘real-life’ experiences.

Petr 2006 Foster care independent living services: youth perspectives

Foster and Former Foster Youth mean age 17.3

– Educational outcome: 26% behind in educational progress and goals, one participant enrolled in GED programme. Increased number of placements often indicates a decrease in educational attainment.

Employment outcome: 10 participants were working at paid jobs in the community. Two participants in the out-of-custody group were working part-time and attending college.

Life skills outcome: 26% of the youth indicated that they had not received any life skills training. Two of the youth indicated it was offered but they refused. 63% of the youth who received the life skills training indicated they were in one of three settings: a class room setting, mental-health agency, or group home facility. These youth also indicated receiving life skills training from foster parents in a less formal, day-to-day basis.

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Table 2 Continued

Study Year Title Study population Study design Primary outcome

Mares 2010 An assessment of independent living needs among emancipating foster youth

Foster and former foster youth

– Educational outcome: Secondary education support was identified as one of the most common services available. However, financial support for college was among the least common service available or offered according to service providers.

Housing outcome: Housing assistance was among the most helpful service identified for youth aging out of care. However, affordable housing and structured transitional housing were identified as gaps for youth within ILPs.

Life skills outcome: Life skills training was identified as being among the most helpful for emancipating youth, however, hands-on life skills training was identified as a gap in service. This is also identified as one of the greatest unmet needs within ILPs.

Powers et al. 2012 My life: Effects of a longitudinal, randomized study of self-determination enhancement on the transition outcomes of youth in foster care and special education

Foster and former foster youth mean age 16.8

OXO O OYO O

Education outcome: 38% of the intervention group and 28% of the comparison group completed secondary education. At 1-year follow-up, 72% for intervention and 50% for comparison group completed secondary education. Three youth were participating in post-secondary education and 26% of youth were participating in post-secondary education at 1-year follow-up.

Employment outcome: 14% of the intervention group and 19% of the comparison group reported working paid jobs at baseline. At post-intervention, 34% of the intervention group and 16% of the comparison group reported working paid jobs. At 1-year follow-up, 45% of the intervention group and 28% of the comparison group had a paid job.

Housing outcome: At post-intervention, 63% of participants were still in foster care, six participants were adopted or reunited with birth family, 14 participants were living with friends or a partner in their own apartment, one participant had housing provided through Job Corps and two participants identified as being homeless. At 1-year follow-up, 57% of participants had exited care, 15 participants reported being reunited or adopted, 14 participants were living in their own apartment, four participants were residing in college dormitories, one participant was in military housing and one participant had housing through Job Corps. 60% of the comparison group reported having a different placement from the year before compared to 50% of the intervention group, indicating a trend toward placement stability.

Mental health/special needs outcome: 40% of the sample had emotional/behavioural problems, 10% of the sample had intellectual disabilities, 16% had speech/language problems, 26% had a learning disability, 5% were considered to be on the autism spectrum, and 26% developmental disabilities services. The youth who received the intervention fared better in high school completion and fared better in employment outcomes.

ILP, independent living programme; XO: indicates the research design included only an intervention and post-test (no pre-test); YO: indicates an alternative design with only an intervention (or treatment as usual) and post-test (no pre-test); OYO: indicates an alternative design with a pre-test, intervention (or treatment as usual), and post-test; OXO: indicates a research design with a pre-test, intervention, and post-test.

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and views on helpful and needed independent living services.The response rate for the survey was 28% (23 respondents).

The study by Kroner & Mares (2011) sampled youth who admitted into and discharged from the Lighthouse ILP from 2001 through 2006. The initial number of youth was 455; however, the final sample size included 367 participants because of the missing data. This experimental study compared two groups – youth with discharge living arrangements and youth without discharge living arrangements. The authors found no statistically significant differences between these two groups at the initial assessments.There were 22 different living arrangement categories divided into four levels of care from lowest (described as living in the most independent and stable housing) to the highest (described as living in the least independent and most unstable housing). The authors used inde- pendent samples t-test and chi-square tests in order to compare youth with living arrangement data to those without such data available. In addition, the use of an analysis of variance (ANOVA) compared characteris- tics of the youth entering the programme from the youth being discharged from the programme.

The study by Mares & Kroner (2011) sampled 385 youth admitted into the Lighthouse ILP during 2001 through 2005. The authors utilized an experimental study comparing youth with mental-health concerns to those without such concerns. The youth had an average age of 17.9 years, with a range from 16 to 20. The length of stay averaged 9.9 months with a range from 0 to 32 months. Sixty-nine per cent of the sample received some life skills training prior to dis- charge, including employment skills training (54%), vocational training (16%), GED preparation (8%) and violence prevention (8%). Originally, the authors examined 22 dichotomous risk factors; however, the six domains pertinent to the study only included 19 risk factors. The six domains included mental health and substance abuse, socialization, delinquency, teen parenting, cognitive impairment, and motivation and health. There were four dichotomous outcomes meas- ured: completing high school or GED, being employed or completing a vocational training pro- gramme, living independently (i.e. renting an apart- ment or home, either alone or with someone else), and completing high school, being employed, and living independently. An exploratory factor analysis identi- fied clinical risk factor groups using the principal component method of extraction.The authors utilized logistic regression to examine the association between clinical risk factors and client outcomes, controlling

for socio-demographic characteristics, length of stay and life skills training.

Finally, the study by Powers et al. (2012) examined an ILP specifically designed for foster youth who received special education services. While this sub- population differs from the general foster care popu- lation, this study was included because it fell within this review’s inclusionary criteria. This study employed a randomized methodology, comparing Foster Care Independent Living Program (FCILP) and TAKE CHARGE. There were four criteria for inclusion, youth must (i) receive special education services; (ii) be between 16.5 and 17.5 years of age; (iii) be under the guardianship of Oregon DHS; and (iv) attend a large school district. The approximate length of stay in the TAKE CHARGE group was 12 months; youth in the FCILP received services as normal. There were 69 participants at baseline (33 in the intervention group and 36 in the comparison group), enrolled over three study waves and randomly assigned to either the treatment or comparison group. There were several outcomes measured at baseline, post-intervention and 1-year follow-up; however, for the purposes of this review, only three will be exam- ined; high school completion, employment and living status. At post-intervention, 60 participants were assessed (29 intervention and 31 comparison), and at 1-year follow-up, 61 participants were assessed (29 intervention and 32 comparison). The attrition rate was 13% at post-intervention and 11% at 1-year follow-up. At baseline, the mean age for the partici- pants was 16.8 years, with 41% of the sample identi- fied as female. Approximately 40% of the participants attended an alternative school and approximately 26% of the participants received developmental disability services.

A R E V I E W O F T H E S I X P R I M A R Y S T U D I E S

Participants’ characteristics

The average age of the participants, calculated using reported mean ages across the six primary studies, was 17.1 years.The studies indicated that youth often have mental-health problems (64% for Kroner & Mares (2011); 47% for Mares & Kroner (2011); and 34% for Mares (2010)) when ageing out of the system, which likely influence post-secondary education, employ- ment, life skills and housing. Five of the six studies included race/ethnicity and gender characteristics of the sample. Three out of the five studies that reported

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race indicated that over 50% of the sample was African-American or non-Caucasian (61% for Uzoebo et al. (2008); 70% for Mares & Kroner (2011); and 62% for Kroner & Mares (2011)). Petr (2006) indicated that 55.5% of …

Attachment 3

The Impact of Victimization and Mental Health Symptoms on Recidivism for Early System-Involved Juvenile Offenders

Lindsey E. Wylie University of Nebraska Omaha

Katrina A. Rufino University of Houston–Downtown and The Menninger Clinic,

Baylor College of Medicine

Although research has linked mental health symptoms and prior victimization to recidivism for youth on probation or in detention, little attention has been given to these risk factors for early system-involved youth. We conducted a survival/hazard model to estimate the impact of official records of abuse/neglect, crime victimization, and mental health issues (mood, anxiety, disruptive, and substance use disorders) on recidivism in a sample of 2,792 youth in a large Midwestern diversion program. Results indicated that youth with official records of abuse/neglect, person crime victimization, and property crime victimization were more likely to recidivate sooner than those without these victimization experiences (hazard ratio: 1.37, 1.42, and 1.52, respectively). Findings from the present study also demonstrated that substance use disorder was the only mental health cluster that predicted quicker time to recidivism. As one of the earliest points of entry into the juvenile justice system, diversion programs are in a unique position to address trauma from multiple types of victimization and adapt diversion programming to be responsive to each juvenile’s mental health needs.

Public Significance Statement Early system-involved youth referred to juvenile diversion had high levels of mental health symp- toms and many had prior experiences with various types of victimization that are based on official law enforcement records. Prior victimization significantly predicted whether a youth had future contact with the juvenile or adult criminal justice system, even while considering other factors, such as risk level and youth characteristics.

Keywords: juvenile recidivism, juvenile diversion, mental health, victimization

In 2016, there were approximately 856,130 juvenile arrests in the United States—many for nonviolent offenses such as larceny–theft, other assaults, drug abuse violations, liquor law violations, vandalism, disorderly conduct, and curfew/loitering (OJJDP, 2016). As such, the juvenile justice system is often tasked with how to address youth who commit less serious offenses. One approach is to divert them away from formal juvenile justice system involvement through diversion programs. As the gateway to the juvenile justice system, diversion programs are in a unique position to address the needs of early system-involved youth, including needs related to victimization and mental health symptoms, to reduce future involvement in the juvenile or adult criminal justice system.

Developmental models of antisocial behavior propose that “delin- quency is marked by a reliable developmental sequence of experi-

ences,” in which childhood experiences and social environment put children at risk for social maladjustment and criminal behavior (Pat- terson, Debaryshe, & Ramsey, 1989, p.263). Specifically, studies find that experiences with victimization, broadly defined as maltreatment, adverse childhood experiences, and general crime victimization, are related to mental health issues (e.g., Abram et al., 2004; Kilpatrick et al., 2000) and that both victimization and mental health issues are related to juvenile justice involvement (e.g., Barrett, Katsiyannis, Zhang, & Zhang, 2014; Fazel, Doll, & Långström, 2008). Although the association of victimization and mental health symptoms within juvenile justice populations are well-documented, especially within samples of serious juvenile offenders (e.g., adjudicated or incarcer- ated), fewer studies have examined these risk factors in a sample of early system-involved youth. The purpose of this study is to examine the relationship between prior victimization, as obtained from official law enforcement records, and mental health symptoms on time to recidivism in a sample of early system-involved youth in a juvenile diversion program.

Operationalizing Victimization

Researchers operationalize victimization using multiple defini- tions. Most studies measure victimization as child maltreatment, utilizing official data obtained from social service agencies or

This article was published Online First November 1, 2018. Lindsey E. Wylie, School of Criminology and Criminal Justice, Juvenile

Justice Institute, University of Nebraska Omaha; Katrina A. Rufino, De- partment of Social Sciences, University of Houston–Downtown and The Menninger Clinic, Baylor College of Medicine.

Correspondence concerning this article should be addressed to Lindsey E. Wylie, Juvenile Justice Institute, University of Nebraska Omaha, 941 O Street, Suite 706, Lincoln, NE 68508. E-mail: [email protected]

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Law and Human Behavior © 2018 American Psychological Association 2018, Vol. 42, No. 6, 558 –569 0147-7307/18/$12.00 http://dx.doi.org/10.1037/lhb0000311

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child protective services (e.g., Barrett et al., 2014; English, Wi- dom, & Brandford, 2002; Smith, Ireland, & Thornberry, 2005), or self-report data obtained from caregivers or youth (e.g., Conrad, Tolou-Shams, Rizzo, Placella, & Brown, 2014). Other studies include broader definitions of victimization, usually measured with self-report data, including adverse childhood experiences (ACEs), such as abuse/neglect, parental divorce, and family violence (e.g., Wolff, Baglivio, & Piquero, 2015; Kilpatrick et al., 2003) or general crime victimization, such as theft or assault (e.g., Finkel- hor, Ormrod, & Turner, 2009; Manasse & Ganem, 2009). Research employing these broader definitions of victimization typically have not included data from official agency records.

As such, the current study expands previous research using a broader definition of victimization, to include abuse/neglect, sex- ual assault, property crimes, and person crimes, utilizing reported incidents of victimization data obtained from official law enforce- ment records. Although official records are likely an underestima- tion of abuse/neglect (Swahn et al., 2006) or general crime trends (see Loftin & McDowall, 2010) because of failure to report or other system-wide factors, using this definition has practical im- plications for programmatic interventions because this information may be readily available to diversion programs, and may produce different findings than studies using self-report data.

Victimization and Mental Health Symptoms

Research demonstrates that victimization as a child or adoles- cent is associated with later mental health problems in both lon- gitudinal studies with representative samples (e.g., Finkelhor et al., 2009; Kilpatrick et al., 2000; Manasse & Ganem, 2009) and retrospective studies with justice-involved samples (e.g., Barrett et al., 2014; Dierkhising et al., 2013; Ford, Grasso, Hawke, & Chap- man, 2013). For instance, in a national random sample of non- justice-involved children ages 2 to 17, Finkelhor and colleagues (2009) examined the Developmental Victimization Survey (DVS) to assess the range of childhood victimizations across five victim- ization types, including conventional crime (e.g., assaults and property crimes), child maltreatment, peer and sibling victimiza- tion, sexual assault, and indirect victimization (e.g., witnessing violence). Overall, 79.6% of the sample reported lifetime victim- ization and analysis revealed a strong association between lifetime poly victimization (the total number of different types of victim- izations) and mental health symptoms (anger, depression, and anxiety).

When examining samples of justice-involved youth, a large proportion of youth report exposure to some type of potentially traumatic event. Dierkhising and colleagues (2013) reported up to 90% of justice-involved youth experienced one or more potentially traumatizing events (e.g., traumatic loss, impaired caregiver, do- mestic violence, school violence), with an average of 4.9 lifetime events. In measuring the link between victimization and mental health problems, Ford and colleagues (2013) found that 58% of the sample endorsed one of the 19 potentially traumatic events (e.g., being in a bad accident, witnessing violence, sexual assault), which was related to posttraumatic stress symptoms, emotional and be- havioral problems, suicide risk, and alcohol and drug use prob- lems.

Victimization and Delinquency

Within criminological perspectives and strain theory, Agnew (1992) argued that criminal victimization is “among the types of strain that are most likely to lead to delinquency” (Agnew, 1992, p.306) because it is perceived as unjust and traumatic, which evokes anger and resentment, and contributes to deviance as a mechanism to cope with strain (Agnew, 1992; Hay & Evans, 2006). Studies have examined whether victimization increases the risk for later delinquency, including general delinquency and vio- lent delinquency, in both representative samples (i.e., longitudinal studies of nonjustice-involved children) and justice-involved sam- ples (i.e., retrospective studies of youth who are justice-involved).

Studies including representative samples found that those with a history of child maltreatment were significantly more likely to have contact with the police as a juvenile or adult than those without a history of child maltreatment (Smith & Thornberry, 1995; Smith et al., 2005; Zingraff, Leiter, Myers, & Johnson, 1993). Although the link between maltreatment and delinquency is consistent across longitudinal studies, the impact of maltreatment on future violence may be less predictive than other factors, including antisocial peers, substance abuse, and family socioeco- nomic status (Hawkins et al., 2000). Furthermore, research dem- onstrates that general crime victimization is associated with delin- quency. For example, using a measure of general crime victimization that included theft, assault, parental physical abuse, attack by a weapon, and property damage, Manasse and Ganem (2009) found that for every one point on their victimization mea- sure, the odds of engaging in delinquency increased by 19%.

Victimization also impacts future reoffending after initial justice involvement. Wolff and colleagues (2015) tested whether exposure to ACEs, measured using a sum of 10 binary absence/presence indicators, significantly predicted time to recidivism following community-based treatment. Overall, having a greater number of ACEs was related to a shorter time to recidivism, even while controlling for youth demographics, and risk factors such as sub- stance abuse, criminal history, deviant peers, and school behavior. Others found that gender may also moderate the relationship between victimization and delinquency. Specifically examining the association between prior sexual abuse and delinquency in a sample of 454 juveniles referred by a judge for a mental health evaluation, Conrad and colleagues (2014) indicated that being sexually abused as a child was the strongest predictor of recidivism for girls but not boys, even while controlling for prior legal involvement and conduct problems.

Mental Health Symptoms

There is growing attention on the high prevalence of mental health problems in the juvenile justice system and researchers have consistently found higher rates of mental health problems in justice-involved youth, than youth in the general population (Abram et al., 2004; Dierkhising et al., 2013; Fazel et al., 2008; Teplin, Abram, McClelland, Dulcan, & Mericle, 2002). A meta- analysis by Fazel and colleagues (2008) examined the results of 25 studies that included interviews with detained juveniles and found that juveniles in detention and correctional facilities were signifi- cantly more likely to have mental health disorders (conduct dis- order, psychosis, attention-deficit/hyperactivity disorder, and ma- jor depression) than age-equivalent juveniles in the general

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559VICTIMIZATION AND MENTAL HEALTH ON RECIDIVISM

population. Overall prevalence rates, however, appear to differ by gender. In a large random sample of youth interviewed during detention intake, Teplin and colleagues (2002) estimated that ap- proximately two thirds of males and three quarters of females met criteria for one or more psychiatric disorders, including disruptive disorders (attention deficit disorders, oppositional defiant disor- der), substance use disorders, and affective disorders.

Although the high prevalence of mental health issues within justice-involved youth has been well-documented, previous re- search has been mixed with respect to whether mental health is predictive of reoffending, and if so, what types of disorders may be most predictive. Cottle, Lee, and Heilbrun’s (2001) meta-analysis of 23 studies that measured recidivism in juveniles indicated that conduct problems (e.g., conduct disorder) and nonsevere psycho- pathology (e.g., stress, anxiety) elevated the risk for recidivism, but severe psychopathology (e.g., psychosis, suicidality) and a history of psychiatric treatment did not increase risk for recidi- vism. On the other hand, in a study of youth referred to probation, the results indicated that while anxiety and mood disorders did not relate to recidivism, both substance use disorders and disruptive behaviors did increase the risk of recidivism (McReynolds, Schwalbe, & Wasserman, 2010).

Trauma-exposed youth who are justice-involved are at risk for several mental health disorders, including posttraumatic stress disorder, major depressive disorder, and substance abuse (Abram et al., 2004; Dierkhising et al., 2013; Ford, Hartman, Hawke, & Chapman, 2008). When testing the role of mental health in the victimization-delinquency link, participants who endorsed depres- sive symptoms were more likely to respond to general crime victimization (i.e., theft, assault, parental physical abuse, attack by a weapon, and property damage) with delinquent behavior. There was a moderating effect of gender, such that males who experi- enced depressive symptoms were 50 times more likely to respond to victimization with delinquency than males without depressive symptoms; but there were no differences for females with depres- sive symptoms (Manasse & Ganem, 2009).

A limitation of these studies, however, is that they only provide support for an association between mental health problems and risk of delinquency, but do not explain the mechanism by which mental health problems increase this risk. Recent research has sought to “disentangle shared risk” to evaluate whether having a mental health problem explained delinquency outcomes above criminogenic risk factors and whether mental health issues mod- erate the relationship between criminogenic risk and delinquency outcomes (Guebert & Olver, 2014; Schubert, Mulvey, & Glasheen, 2011). These studies demonstrated that even though the presence of mental health problems was related to reoffending in bivariate analyses, after controlling for criminogenic risk markers and de- mographics, mental health issues did not uniquely contribute to reoffending above these other factors (Guebert & Olver, 2014; Schubert et al., 2011).

The Current Study

The present study utilized a sample of early system-involved youth referred to a juvenile diversion program in a large Midwest- ern city. The purpose of this study was to examine reoffending for youth with reported experiences of victimization, as well as mental health symptoms at the time of diversion intake. Although research

has examined the recidivism trajectory of youth at the deeper end of the juvenile justice system, fewer studies have linked victim- ization and mental health problems to recidivism in a sample of early system-involved youth. Juveniles in the diversion program are typically first-time offenders referred because of minor of- fenses (e.g., shoplifting, possession of marijuana, status offenses) and assessed as low to moderate risk. The present research con- tributes to the larger body of literature by examining whether the association between victimization, mental health problems, and recidivism is similar for early system-involved youth to better inform diversion efforts. Furthermore, the present study extends prior research by including a broader measure of victimization that includes abuse/neglect, sexual assault, property crime, and person crimes that have been reported to law enforcement.

Method

Participants

Participants included 2,792 justice-involved juveniles referred for diversion in a large Midwestern city. The mean age was 15.08 (SD � 1.64) and the majority were male (59.7%, n � 1,668). Approximately half identified as White (48.4%, n � 1,352), fol- lowed by Black (34.1%, n � 953), Hispanic/Latino (14.6%, n � 407), Asian/Pacific Islander (1.1%, n � 31), Native American/ Alaskan Native (1.1%, n � 31), and other or multiple races (0.6%, n � 17). Most participants were referred to diversion for drug or alcohol-related offenses such as possession of marijuana or para- phernalia (35.2%, n � 984) and property offenses, such as shop- lifting and theft (35.5%, n � 990). Other offenses included disor- derly conduct (11.7%, n � 326), crimes against others such as third-degree assault (9.5%, n � 265), traffic offenses such as driving without a license (2.5%, n � 69), and other offenses such as vandalism, curfew violation, providing false information to the police, and obstructing an officer (5.7%, n � 158). Although youth may be referred to diversion for truancy in this county, this sample does not include those youth, because truancy diversion is a separate program that utilizes a different assessment process. If youth successfully complete the diversion program, the county attorney does not file their case and they are not adjudicated delinquent.

Study Design and Procedure

Data were obtained from the juvenile diversion program’s case management system as part of a statutorily required statewide evaluation of juvenile justice-related programs that receive fund- ing from the state. The data included identifying information (e.g., name and date of birth) so that we could compute recidivism, as required under statute for the statewide program evaluation. Insti- tutional review board approval was obtained by the University of Nebraska Medical Center as part of the program evaluation.

Between July 1, 2012 and June 30, 2015, a total of 3,934 juveniles were referred to the juvenile assessment center for pos- sible participation in juvenile diversion following a law violation. Once a juvenile receives a law violation, if eligible based on evidentiary factors and the type of offense, the county attorney will refer the case to the assessment center to determine whether the youth should participate in the diversion program. Of the youth

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560 WYLIE AND RUFINO

referred during this time, 2,792 were eligible and decided to participate in diversion, which is the total sample for the study. Youth may be referred, but not enroll for various reasons includ- ing: receiving a warning letter if they are screened as lower risk, they were deemed not eligible by the diversion program, the youth or family refused to participate, or procedural reasons (e.g., out of jurisdiction, recommend nolle pros, the youth received a new charge while awaiting assessment, or the county attorney withdrew the referral).

During the assessment process, each juvenile completes several assessments and the assessment specialist creates a diversion plan based on each juvenile’s risk and needs. If the youth successfully completes the diversion plan, their case is dismissed and not filed in juvenile court. If a youth is not successful, either because he or she did not complete the diversion plan requirements or receives a new law violation while on diversion, then the case is filed and the youth goes through the traditional juvenile court process.

Measures

Demographics. We included age, gender, and race/ethnicity as demographic controls. Age was measured continuously as the youth’s age at the time of referral. Gender was dichotomous (0 � male, 1 � female). Race/ethnicity was measured using a dichot- omous variable with 0 � White and 1 � non-White.

Successful completion of diversion. To control for success- ful completion of the diversion program, which may influence future reoffending, we included a dichotomous variable for suc- cessful discharge (0) or unsuccessful discharge (1) as measured within the juvenile diversion program’s case management system. Overall, 83.5% of youth successfully completed their diversion program requirements.

Risk-level. To measure each juvenile’s level of risk, the Youth Level of Service/Case Management Inventory 2.0 (YLS/ CMI 2.0; Hoge & Andrews, 2011) was used. The YLS/CMI 2.0 is a 42-item checklist designed to be completed by a mental health professional or a probation officer utilizing interviews and/or record reviews. The measure provides a total score and a score for each of eight subscales including: Offense History, Family, Edu- cation, Substance Abuse, Leisure/Recreation, Peer Relations, Per- sonality/Behavior, and Attitudes/Orientation. Each item is coded as present or absent for a total score ranging from 0 to 42. These total scores determine if the juvenile is low (0 through 8), moderate (9 through 22), high (22 through 34), or very high (35 through 42) risk for recidivism. The information for each item is gathered from the youth and family directly, and through collateral information (e.g., the school, other agencies). The assessment tool is for youth aged 12 to 18 years old, therefore youth in the sample younger than 12 were not assessed via the YLS/CMI. The manual provides evidence of strong reliability and validity (Hoge & Andrews, 2011) and was validated elsewhere (Onifade et al., 2008; Vincent, Guy, & Grisso, 2012). On average, the sample was a low to moderate risk group with a mean YLS/CMI-total score of 7.46 (SD � 4.21). Most of the sample scored in the low risk range (65.7%) or moderate risk range (34.2%), with only three partici- pants scoring in the high-risk range (0.1%) and none in the very high-risk range.

Official records of victimization. As part of the diversion program’s case management system, data is automatically pulled

from law enforcement any time the youth is listed as a victim. As such, the measure of victimization includes only reported incidents that involved law enforcement. Each incident of victimization was recoded into one of four categories: sexual assault, abuse/neglect, property crime (e.g., burglary, theft, robbery) victimization, and person crime (e.g., assault) victimization. Property and person crimes were classified based on the FBI Uniform Crime Reporting Program (U.S. Department of Justice, 2016), with person crimes including those where the individual is the direct victim and property crimes including instances where the object is to obtain money or some other benefit. Overall, 16.3% of the sample had at least one occurrence of reported victimization, 1.9% had two occurrences, and 0.3% had three occurrences of reported victim- ization. The prevalence of each victimization type was: abuse/ neglect (6.9%), sexual assault (1.7%), victim of a person crime (9.9%), and victim of a property crime (2.6%).

Mental health symptoms. The DISC Predictive Scales (DPS; Lucas et al., 2001) was used to measure each juvenile’s presenting symptoms at the time of their assessment. The DPS was developed as an efficient diagnostic screening tool for juveniles and identifies youth who are highly likely to meet diagnostic criteria (McReyn- olds, Wasserman, Fisher, & Lucas, 2007). The DPS was validated in a community and mixed sample of “troubled” 10- to 18-year-old youth of both genders (Lucas et al., 2001) and with justice- involved juveniles (McReynolds et al., 2007). The DPS is a com- puterized self-report tool that uses audio to read each question. The number of questions for each module varies depending on the rule-out criteria and follow-up questions based on flagged re- sponses. The DPS derives from the most sensitive questions con- tained in the Diagnostic Interview Schedule for Children-2.3 (DISC; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) to determine if symptoms are “present,” “possible,” or “absent” within the last year.

For the purposes of these analyses, we combined “possible” and “absent” for a dichotomous measure of each symptom as either present (1) or possible/absent (0). For each of the symptoms, we organized them into four clusters similar to previous studies (McReynolds et al., 2007, 2010; Wasserman, McReynolds, Lucas, Fisher, & Santos, 2002). The clusters utilized in the analyses were: disruptive behaviors (attention deficit hyperactivity, oppositional defiant, and conduct disorder), substance use (alcohol use, mari- juana use, and other substance use), anxiety (posttraumatic stress disorder, agoraphobia, social phobia, general anxiety, obsessive– compulsive disorder, specific phobia, and panic disorder), and mood (depression and mania). Overall, 63.8% of the juveniles in this sample endorsed one or more mental health symptom clusters: 41% endorsing the disruptive cluster, 35.3% the anxiety cluster, 21.7% the substance use cluster, and 19.6% the mood cluster.

Recidivism. Recidivism data were obtained from the state’s trial court case management system and was defined as any of- fense that was filed in court following discharge from diversion, excluding cases that were eventually dismissed. Data included all juvenile and adult misdemeanor and felony cases between July 1, 2012 and December 31, 2015, including sealed records. These dates allowed at least a 6-month recidivism period for juveniles enrolling in diversion at the end of the study period (June 30, 2015). The time at risk from discharge to the end of the study period ranged from 180 days to 1,271 days, with a mean of 850.83 days (SD � 389.60 days, Mdn � 859.00 days).

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561VICTIMIZATION AND MENTAL HEALTH ON RECIDIVISM

Using probabilistic record linkage software, we matched youth in the sample to the recidivism records using first name, middle name, last name, and date of birth, which accommodates mis- spelled names or typos with dates of birth. We included any offenses, including status offenses, that may bring a youth back in to the juvenile justice system or diversion, which in this state, includes truancy offenses eligible to be filed on in court (20 or more absences that are not medically related). Of those who recidivated (n � 839, 30.1%), the most common offenses included drug or alcohol-related offenses (n � 255, 30.4%) and property offenses (n � 229, 27.3%); the remaining offenses included dis- orderly conduct (7.2%, n � 60), crimes against others such as assault and robbery (7.7%, n � 67), traffic offenses such as driving without a license (2.5%, n � 21), truancy (10.7%, n � 90), and other offenses such as vandalism, curfew violation, providing false information to the police, and obstructing an officer (13.9%, n � 117).

Data Analysis and Hypotheses

First, several bivariate analyses were conducted to compare juveniles who endorsed victimization and those who did not. The victimization types, gender, and reason for discharge were com- pared to each other and with each mental health cluster using a chi-square test due to the dichotomous nature of the variables. Differences for gender and victimization on age and risk level were compared using analysis of variance. To test the relationship of the independent variables while controlling for demographic factors and risk level, data were analyzed using a hazard Cox regression analysis to predict time to failure (i.e., recidivating event), which provides more details than a simple dichotomous indicator of recidivism because it specifically targets the amount of time to reoffending (Wolff et al., 2015). Prior research has indi- cated that the time to the recidivating event is an important outcome to consider because the individual characteristics of ju- veniles who recidivate sooner may be different than the charac- teristics of juveniles who recidivate later (Maltz, 1984; Schmidt & Witte, 1989).

The model specifically included the four victimization types and the four mental health symptom clusters across gender and race/ ethnicity, while …

Attachment 4

Article

Treatment Services in the Juvenile Justice System: Examining the Use and Funding of Services by Youth on Probation

Clair White 1

Abstract Youth enter the juvenile justice system with a variety of service needs, particularly for mental health problems. Research has examined the extent to which youth have mental health disorders, primarily among detained youth, and factors associated with treatment referrals, but little research has examined youth on probation and the actual use of services. Using data obtained from the Maricopa County Juvenile Probation Department from July 2012 through August 2014 (N ¼ 3,779), the current study examines (1) the factors associated with receiving treatment services while on probation and (2) the factors associated with receiving treatment services through different funding streams. Findings reveal that only about 25% of the sample of youth on probation received treatment services, suggesting the underservicing of youth. Consistent with prior research, there were also racial and ethnic disparities concerning treatment use, with Blacks and Latinos less likely to receive services. Additionally, certain characteristics of youth and their background influenced the funding source for treatment services. Implications for policy and research are discussed in light of these findings.

Keywords probation, treatment services, service use, juvenile justice, racial/ethnic disparities

The juvenile justice system has multiple responsibilities often serving conflicting goals of punitive

sanctions and rehabilitative treatment (Bishop, 2006; Lipsey, Howell, Kelly, Chapman, & Carver,

2010). The system must not only address the current delinquent behavior but also, in many cases,

consider the health and well-being of the youth. Youth come into the juvenile justice system with

more complex problems and greater needs for mental and behavioral health services, which has

resulted in more attention on efforts to rehabilitate and address youth’s mental and behavioral

1 Center for Evidence-Based Crime Policy, Criminology, Law and Society, George Mason University, Fairfax, VA, USA

Corresponding Author:

Clair White, Center for Evidence-Based Crime Policy, Criminology, Law and Society, George Mason University, 4400

University Dr., MS 6D12, Fairfax, VA 22030, USA.

Email: [email protected]

Youth Violence and Juvenile Justice 2019, Vol. 17(1) 62-87 ª The Author(s) 2017 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/1541204017728997 journals.sagepub.com/home/yvj

service needs (Myers & Farrell, 2008). Research has examined a number of issues related to mental

health and behavioral health problems of youth in the juvenile justice system, particularly identify-

ing the rates of mental health problems and service needs among youth and factors associated with

treatment referrals of youth in different systems of care (i.e., juvenile justice system and mental

health system).

Research on mental health problems in justice-involved youth has primarily focused on the

service needs of youth and where they have been referred to meet these needs and not on whether

they actually received those services. Additionally, much of the work examines youth in detention or

compares youth sentenced to community versus correctional supervision rather than youth on

probation which is the predominate sentence in the juvenile justice system. The current study uses

juvenile probation data from a large, urban jurisdiction in Arizona to examine these issues. More

specifically, legal and extralegal factors associated with the use of treatment services among youth

on probation supervision are examined. Furthermore, the extent to which services are funded by the

juvenile justice system has not been empirically examined, therefore, whether these services are

funded by the juvenile justice system or external funding sources such as Medicaid or private

insurance is also examined.

Unmet Service Needs and Treatment Referrals

Youth involved in the juvenile justice system often experience multiple adversities or risk factors,

such as economic disadvantage, experiences of abuse and neglect, unstable family environments,

exposure to drugs and alcohol, and mental illness (Esbensen, Peterson, & Taylor, 2010; Huizinga,

Loeber, Thornberry, & Cothern, 2000; Loeber & Farrington, 1998). Research has generally found

that 65–70% of youth in juvenile justice facilities, primarily detention centers and correctional facilities, suffer from at least one mental health disorder (Shufelt & Cocozza, 2006; Teplin, Abram,

McClelland, Dulcan, & Mericle, 2002; Wasserman, McReynolds, Lucas, Fisher, & Santos, 2002),

while rates among youth on probation are approximately 50% (Wasserman, McReynolds, Ko, Katz, & Carpenter, 2005).

Additionally, comorbidity, or the presence of more than one mental or behavioral disorder, is

particularly high among youth in juvenile justice settings (Abram, Teplin, McClelland, & Dulcan,

2003; Kessler et al., 1996; Teplin et al., 2002). Shufelt and Cocozza (2006) found that roughly 79% of those who met criteria for at least one mental health disorder had two or more diagnoses.

Unfortunately, many of these mental and behavioral service needs are not met in the community

(Flisher et al., 1997; Jensen et al., 2011; Kataoka, Zhang, & Wells, 2002; Ringel & Sturm, 2001). As

a result, the coexistence of multiple disorders in addition to other criminogenic risk factors makes

prioritizing mental and behavioral service needs more challenging for the juvenile justice system

(Grisso, 2004).

Research has examined factors related to unmet service needs and the avenues through which

youths’ mental health needs are met through various service sectors, such as the mental health

system and juvenile justice system (Burns et al., 2004; Stahmer et al., 2005; Thompson, 2005).

Among the general population, children and adolescents with mental and behavioral health problems

are gravely undertreated with high rates of unmet service needs (Angold et al., 1998; Flisher et al.,

1997; Horwitz, Gary, Briggs-Gowan, & Carter, 2003). Studies have examined characteristics of

children with unmet mental health needs and their families using various samples to identify key

predictors of treatment service use and unmet service needs.

Among the primary factors associated with unmet service needs are elements related to economic

disadvantage such as living on public assistance, lack of health insurance, and transportation prob-

lems (Chow, Jaffee, & Snowden, 2003; Cornelius, Pringle, Jernigan, Kirisci, & Clark, 2001; Haines,

McMunn, Nazroo, & Kelly, 2002). Race and ethnicity are also strong predictors of unmet service

White 63

needs with Whites being more likely to receive mental health services compared to minorities

(Angold et al., 2002; Garland et al., 2005; Kataoka et al., 2002; Thompson, 2005; Yeh, McCabe,

Hough, Dupuis, & Hazen, 2003). Studies have also found that minorities have limited opportunities

to access mental health services (Arcia, Keyes, Gallagher, & Herrick, 1993), and once they start

treatment they are less likely to complete treatment (Kazdin, Stolar, & Marciano, 1995).

Research has also found involvement in the mental health system increases the likelihood of

being referred to the juvenile justice system (Cohen et al., 1990; Evens & Stoep, 1997; Rosenblatt,

Rosenblatt, & Biggs, 2000). In addition, younger adolescents, females, and White youths are more

likely to be referred to the mental health system, while minorities, males, and youths with more

serious and disruptive mental health disorders are more likely to be referred to the juvenile justice

system (Atkins et al., 1999; Cohen et al., 1990; Dembo, Turner, Borden, & Schmeidler, 1994; Evens

& Stoep, 1997). In general, service needs of disadvantaged and minority youth are often not

recognized until their contact with the juvenile justice system (Golzari, Hunt, & Anoshiravani,

2006; Rawal, Romansky, Jenuwine, & Lyons, 2004; Rogers, Pumariega, Atkins, & Cuffe, 2006).

Upon entering the juvenile justice system, service needs often continue to go unmet even after

identification of need for treatment (Rogers, Zima, Powell, & Pumariega, 2001; Shelton, 2005).

Shelton (2005) found that only 23% of youth diagnosed with mental health disorders received treatment and that having a mental disorder was not a significant predictor of receiving services.

A recent study conducted by Hoeve, McReynolds, and Wasserman (2014) found that youth with

externalizing disorders and substance use disorders were more likely to receive referrals, while only

40% of youth with internalizing disorders referred to service. Consistent with the findings from the general public, Whites are more likely to be referred to services compared to Black youth in the

justice system (Dalton, Evans, Cruise, Feinstein, & Kendrick, 2009; Lopez-Williams, Stoep, Kuro,

& Stewart, 2006; Maschi, Hatcher, Schwalbe, & Rosato, 2008; Rogers et al., 2006), but there are

some mixed findings (Breda, 2003; Hoeve et al., 2014). Shelton (2005) concluded that

while the total responsibility for the well-being of children does not lie solely with the juvenile justice

system, the decision not to provide treatment services to youth in need and under their care implies

neglect . . . it implies a perception that these youth will go away, be treated elsewhere, or grow out of their

problems. (p. 110)

These prior studies do not provide a clear set of predictors for service referrals and many studies

were not able to control for offense severity and criminal history (Dalton et al., 2009; Lopez-

Williams et al., 2006; Rawal et al., 2004), which are likely to influence referrals for services.

Regardless, there were discrepancies in service referrals in the juvenile justice system. Receipt of

service referrals was not found to be dependent entirely on the need for services but may be

influenced by other factors that create disparities in the health of youth. Furthermore, these studies

did not take into account access (i.e., availability, health insurance, etc.) to referred services or

whether youth were actually using the services.

Many of the studies previously discussed use referrals for treatment services as the outcome of

interest, but little research has examined the actual receipt or use of treatment services by youth

(Teplin, Abram, McClelland, Washburn, & Pikus, 2005). Teplin, Abram, McClelland, Washburn,

and Pikus (2005) found that roughly 16% of youth who had been identified as needing mental health services during detention received services within 6 months from detention or by disposition.

Additionally, 11% of youths received services but did not meet the definition of need. Johnson et al. (2004) examined substance abuse treatment need and use among youth entering juvenile

corrections and found that nearly half of youth with need for substance abuse treatment received

services. Rawal, Romansky, Jenuwine, and Lyons (2004) examined racial differences in mental

health needs and service use among incarcerated youth. The authors found that Blacks had the

64 Youth Violence and Juvenile Justice 17(1)

greatest level of mental health needs, but the lowest level of prior and current service use. In general,

these studies emphasize how few individuals actually receive services for their mental and beha-

vioral service needs as well as the “benign neglect” of the juvenile justice system in addressing

mental and behavioral service needs (Herz, 2001).

Lastly, receiving referrals for treatment or participating in certain programs and treatment does

not necessarily translate into needs being met (Grisso, 2004). The justice system has the difficult task

of distinguishing youths’ need for specific programs that target criminogenic risk factors from the

need for treatment services that address their overall mental well-being. Given limited training and

resources, some needs are often prioritized over others, leaving other needs unaddressed (Haqanee,

Peterson-Badali, & Skilling, 2015). Responsivity is a key component of the risk-needs-responsivity

(RNR) model in offender treatment, emphasizing matching program and treatment plans to meet the

unique reoffending risks and risk factors (i.e., criminogenic needs) of offenders through evidence-

based rehabilitative programs that are tailored to an individual’s strengths and capacities (Andrews

& Bonta, 2010; Andrews, Bonta, & Hoge, 1990; Hoge & Andrews, 1996). Rather than general

mental health (GMH) care, the RNR model is focused on reducing future delinquency and recidi-

vism but has been criticized for not addressing more basic, noncriminogenic, human needs, such as

mental health (T. Ward & Stewart, 2003; T. Ward, Yates, & Willis, 2012). Additionally, treating

mental health and substance abuse disorders may or may not address other criminogenic risk factors

and prevent future delinquency (see Wibbelink, Hoeve, Stams, & Oort, 2017) but may have impli-

cations for youths’ responsiveness to treatment goals and success in addressing criminogenic needs

(Haqanee et al., 2015). Nevertheless, programs that adhere to the principles of RNR have been

successful in reducing recidivism (Andrews & Bonta, 2010).

One of the primary RNR assessment tools, the Youth Level of Service/Case Management Inven-

tory (YLS/CMI), has been validated for its ability to predict recidivism among youth (Catchpole &

Gretton, 2003; Jung & Rawana, 1999; Onifade et al., 2008; Vieira, Skilling, & Peterson-Badali,

2009). However, agencies and practitioners face many challenges to develop clear treatment plans

and effectively implement services despite identifying risks and needs through assessment (Flores,

Travis, & Latessa, 2004; Latessa, Cullen, & Gendreau, 2002; Sutherland, 2009), resulting in many

youths’ needs left unaddressed (Vieira et al., 2009). This “implementation gap” is often the result in

the availability of quality, evidence-based programming, such as cognitive behavioral therapy

(Haqanee et al., 2015). For example, Flores, Travis, and Latessa (2004) found in one state jurisdic-

tion that the RNR tool (YLS/CMI) was widely used, but when it came to services in the treatment

plans, they rarely targeted the needs identified in the assessment. In sum, there have been great

strides in recognizing and measuring criminogenic risks and needs that when addressed can improve

outcomes for youth. Mental illness, however, is often not considered one of those criminogenic

needs (Haqanee et al., 2015), so practitioners may continue to use their clinical judgment and

experience over the use of risk assessment tools (C. Schwalbe, 2004), and services received may

not target the needs/risks identified.

Funding Treatment Services

While the juvenile justice system has a legal mandate to provide treatment services, it does not have

to be the one to administer that care (Grisso, 2004). When a youth is required to receive court-

ordered treatment services as a condition of probation supervision, there are multiple avenues or

sources of funding that can pay for these services. If the youth has no means (i.e., health insurance)

to pay for treatment services ordered by the court, the juvenile justice system has a financial

responsibility to fund the treatment services it is requiring.

The juvenile justice system has used outside agencies and external funds to reduce the burden of

providing treatment services—they typically contract out to private providers or other government

White 65

agencies such as public mental health service providers. Similarly, the treatment services can be

funded through different sources such as private insurance or public health care, but if those avenues

are not available, the juvenile justice system is responsible to fund the treatment services. Families

of youth in the juvenile justice system often have limited knowledge and resources to navigate the

health-care system; therefore, youth often are more likely to be uninsured and their mental and

behavioral conditions are not addressed. Furthermore, services provided through Medicaid are often

restricted to children with the most severe mental disorders due to lack of funding (Kerker & Dore,

2006). As a result, children with less serious problems are often ineligible for services and those who

do qualify receive inconsistent and fragmented care. Finally, studies have found that lack of health

insurance is a major impediment to obtaining mental and behavioral health services (Farmer, Stangl,

Burns, Costello, & Angold, 1999; Flisher et al., 1997; Kataoka et al., 2002).

In light of the health-care debate, the current research also speaks to the issue of funding and

resources for mental health care and substance use disorder services that are often subject to social,

political, and economic influence. The coverage for mental health and substance use disorders by

insurance companies and the availability and eligibility of Medicaid will likely have implications for

practices in the juvenile justice system and the extent to which treatment services are court-funded.

If youth have alternative sources to pay for treatment services, such as private insurance or Med-

icaid, the juvenile justice system will be relieved of that responsibility. While the current research

does not empirically evaluate health-care reform on funding treatment services in the juvenile justice

system, findings should be considered in the context of these broader changes.

The funding of treatment services in the juvenile justice system has not been examined as a key

variable of interest. While the source of funding for treatment services is often determined by the

youth’s health care coverage, the court also considers the need for services, prioritizing those with

greatest need. However, as was demonstrated with literature on unmet service needs, need does

not necessarily result in the expected outcomes (i.e., services). Following this line of thought,

there may be other factors that could influence the court’s decision to fund treatment services.

Furthermore, the quality of services and degree of investment the court has when it is funding the

treatment services may differ, which may have implications for the future delinquent behavior and

overall health of the youth.

Current Focus

Building on previous research on service needs and use among youth with mental and behavioral

problems, this research examined treatment services received by youth involved in the Maricopa

County Juvenile Probation Department (MCJPD). The court serves youth by requiring treatment

services for mental and behavioral problems but providing resources to pay for treatment services

adds an additional level of intervention and investment in these youth’s lives. The current research

examined characteristics of youth who received treatment services as well as funding sources for

services. More specifically, two research questions are examined:

Research Question 1: What are the predictors (e.g., gender, race, delinquent background,

etc.) associated with receiving treatment services under probation supervision?

Research Question 2: Among youth receiving treatment services, what are the predictors

associated with the source of funding for treatment services; specifically, what are the pre-

dictors of receiving treatment services via external funding sources relative to court-based

funding?

Research on mental and behavioral service needs and service referrals has generally focused on

treatment for mental health and substance use disorders, but youth can have other service needs. The

66 Youth Violence and Juvenile Justice 17(1)

current research is not restricted to mental health and substance use treatment services and is

more inclusive of other treatment services provided by the juvenile justice system, such as

behavior-specific education, mentoring programs, and evidence-based programs. Based on previous

research, we expect race/ethnicity to be a strong predictor of service use as well as prior history of

mental health problems and involvement in the juvenile justice system.

This research will also shed light on which types of services are typically funded by the court. The

ever-changing financial climate and the health-care debate provide a broader context that can help

inform the importance of understanding the sources of funding for treatment services. There is

growing concern for addressing service needs, particularly for mental health and substance use

disorders, but with limited resources, the funding sources of treatment services deserves empirical

attention. Given the limited attention on the issue of funding, this question is more exploratory in

nature. The implications of this research will help to inform broader issues of the juvenile justice

system’s obligation to provide treatment.

Method

Data and Sample

The MCJPD and the Treatment Services Division were sources for data regarding youth receiving

treatment services. The time frame for the data spanned a 25-month period beginning July 1, 2012, to

August 31, 2014, during which a total of 4,244 youth were placed on probation, 60 of whom had

multiple probations during the time frame. 1

The data were compiled onsite with the assistance from

the Research and Planning Division of the MCJPD. A data sharing agreement was obtained with

institutional review board approval to receive deidentified youth information through electronic

databases. With the exception of certain files, such as psychological case notes, 2

MCJPD uses the

integrated court information system to manage youths’ records, and Microsoft ®

Access was used to

query databases associated with youth who were placed under probation supervision during the

specified time frame. 3

For purposes of this analysis, the unit of analysis was the individual youth. Eight different databases

were used to measure the legal and extralegal characteristics of the youth and their case. The databases

were cleaned as separate files and merged based on each youth’s unique identifier. The data required

recoding variables and MCJPD advised to ensure the recoded variables accurately measured the

correct information. For example, the complaint data set contained all referrals (or complaints) the

youth has received in Maricopa County. The unit of analysis in this data set was referrals, and there

were 17,784 referrals for the 4,244 youth analyzed in the current research. This data set, in particular,

took an extensive amount of cleaning and management because it was used to (1) identify which

referral was associated with the disposition that placed the youth on probation and the severity of that

offense, and (2) determine the number of referrals and adjudications that occurred before the current

probation to measure prior offending behavior.

The final sample of youths on probation was 3,779 after those with short probation periods (less

than 10 days) and cases with missing data were removed. 4

Descriptive statistics of the sample of

youth are presented in Table 1. Similar to other research on juvenile justice populations, a majority

of the sample was male (81.2%), roughly 37% of the sample were White, 15% Black, and 41% Hispanic, and the mean age was 16.1 years old. A majority of the youth came from single parent

living situations (60.8%) and a quarter were not enrolled in school. In regard to the youths’ offense and juvenile justice history, property felonies were the most common (25.1%), followed by personal felonies (19.1%), 40.5% were detained prior to adjudication, 67.1% had a prior referral, and 12.9% had a prior adjudication. Additionally, 37.5% of youth received a psychological evaluation

White 67

associated with the current offense, 18.3% had prior treatment services, and in regard to risk level, 20.4% were low, 24.6% were moderate, and 55% were high risk.

The current research focused on youth who received treatment services in the community and

residential facilities while on probation, thus services received while on diversion will not be

examined, but will be captured as prior services. In 2012, MCJPD began the Service Authorization

Table 1. Descriptive Statistics of Dependent and Independent Variables.

Variables

Youth on Probation (N ¼ 3,779)

% n

Outcome variables Receiving treatment services 25.0 944 Funding source (n ¼ 861)

Court-based 72.2 622 External 27.8 239

Independent variables Gender

Female (reference) 18.8 712 Male 81.2 3,067

Race/ethnicity White (reference) 37.4 1,414 Black 15.3 580 Latino 41.4 1,564 Native American 4.3 161 Other 1.6 60

Age (mean) 16.1 (1.3) 3,779 Living situation

Single parent (reference) 60.8 2,299 Two parents 19.6 741 Grandparents or other relatives 8.3 313 DCS and other 11.3 198

School status Enrolled (reference) 75.1 2,839 Not enrolled 24.9 940

Offense severity Property felony (reference) 25.1 948 Personal felony 19.1 720 Property misdemeanor 12.6 477 Personal misdemeanor 8.0 304 Drugs 18.8 710 Public peace 14.8 559 Other 1.6 61

Preadjudication detention 40.5 1,530 Prior referral 67.1 2,537 Prior adjudication 12.9 486 Psychological evaluation 37.5 1,416 Prior treatment services 18.3 692 Risk level

Low (reference) 20.4 770 Moderate 24.6 929 High 55.0 2,080

Note. DCS ¼ Department of Child Services.

68 Youth Violence and Juvenile Justice 17(1)

Form Automation Project to electronically track the treatment services ordered by the court and

progress of youth receiving services as part of their probation. Based on the recommendation by

Research and Planning Division, services that started 90 days prior to the start of probation will also

be considered prior services. Treatment services evaluated in the current study include GMH

services, sex offender services, substance abuse services, mentoring or life skills programs,

behavior-specific education, evidence-based programs, and drug court services. 5

Treatment services

that are not included in the current research include mandatory drug testing, detention alternative

programs, physical health services such as acute care or hospitalization, polygraph examinations,

and assessments. These services were not included because they are not therapeutic in nature and

generally not used to address mental and behavioral service needs. Among the 3,779 youth on

probation included in the analysis, 944 (25%) received the services of interest.

Measures

There are a number of legal and extralegal factors that have been examined in relation to various

outcomes in the juvenile justice system and whether youth end up in mental health system versus

juvenile justice system (Cohen et al., 1990; Evens & Stoep, 1997; Lyons, Baerger, Quigley, Erlich,

& Griffin, 2001; Thomas & Stubbe, 1996). The current study focused on the referral that placed the

youth on the current probation and treatment services, but characteristics of prior behavior are

captured. The independent variables that were used in the analyses include gender, race, ethnicity,

age, living situation, school status, offense severity, preadjudication detention, prior referrals, prior

adjudications, whether the youth received a psychological evaluation, prior treatment service use,

and risk assessment level.

Gender was coded as 1 for males and 0 for females, and race and ethnicity are measured by

several dummy variables: Blacks, Latino/Latina, and other race/ethnicity, with White as the refer-

ence category. Age is measured as the age of the youth at the time of the referral that received a

disposition of treatment services and is measured continuously. The living situation of the youth

captured who the youth lived with when they were placed on probation. The categories included

single parent, two parents, grandparents or other relative, and Department of Child Safety or other,

with single parent serving as the reference category. School status was measured on the basis of

whether or not the youth was enrolled in school during the time of the current referral. Offense

severity captured the most severe offense associated with the referral. Consistent with sentencing

research on juveniles, if the youth was charged with multiple offenses, the most serious offense was

measured. There are seven categories of offense severity—property felony, personal felony, prop-

erty misdemeanor, personal misdemeanor, drugs, public peace, and other offenses that included

obstructions of justice and status offenses. Property felony serves as the reference category because

it had the highest frequency. Preadjudication detention captured whether the youth was detained

prior to adjudication for the current offense and probation. Prior referrals and prior adjudications

are measured dichotomously, with “yes/no” outcomes. Prior service use was also a binary variable,

measuring whether the youth has received treatment services through the court from either diversion

or prior probations.

Every youth who reaches adjudication and disposition is considered for a psychological evalua-

tion, but these are predominately conducted only when there is a history of mental illness and service

need, and the court would benefit from clinical assistance. Therefore, having a psychological

assessment is a strong proxy for history of mental health problems …

Attachment 5

E M P I R I C A L R E S E A R C H

Youth Pathways to Placement: The Influence of Gender, Mental Health Need and Trauma on Confinement in the Juvenile Justice System

Erin M. Espinosa • Jon R. Sorensen •

Molly A. Lopez

Received: 9 April 2013 / Accepted: 27 June 2013 / Published online: 4 July 2013

� Springer Science+Business Media New York 2013

Abstract Although the juvenile crime rate has generally

declined, the involvement of girls in the juvenile justice

system has been increasing. Possible explanations for this

gender difference include the impact of exposure to trauma

and mental health needs on developmental pathways and

the resulting influence of youth’s involvement in the justice

system. This study examined the influence of gender,

mental health needs and trauma on the risk of out-of-home

placement for juvenile offenders. The sample included

youth referred to three urban juvenile probation depart-

ments in Texas between January 1, 2007 and December 31,

2008 and who received state-mandated mental health

screening (N = 34,222; 30.1 % female). The analysis

revealed that, for both genders, elevated scores on the

seven factor-analytically derived subscales of a mental

health screening instrument (Alcohol and Drug Use,

Depressed-Anxious, Somatic Complaints, Suicidal Idea-

tion, Thought Disturbance, and Traumatic Experiences),

especially related to past traumatic experiences, influenced

how deeply juveniles penetrated the system. The findings

suggest that additional research is needed to determine the

effectiveness of trauma interventions and the implemen-

tation of trauma informed systems for youth involved with

the juvenile justice system.

Keywords Detention � Incarceration, disposition � Gender disparity � Trauma � Mental health

Introduction

Adolescence is a period of developmental transition char-

acterized by changes in family, school, peers, self-concept,

and general physical development (Bergman and Scott

2001). Although most youth navigate this developmental

period successfully, incidents of rule breaking and behav-

ioral problems are common and can result in involvement

with law enforcement. Some research suggests that inter-

vention by the criminal justice system during the critical

period of adolescence may negatively impact youth out-

comes, including decreasing opportunities for meeting

educational goals and increasing the risk for later

involvement in delinquency and deviance (Sampson and

Laub 2005; pipeline articles). Recent trends have shown a

steady decline in juvenile offending overall, particularly

among violent crimes. However, statistics have also shown

a trend toward increased delinquency in females. For

example, Snyder (2008) reported that between 1994 and

2006, arrests for simple assault declined by 4 % for boys

while the rate increased by 19 % for girls. Given the

gender differences in adolescent development, it seems

critical to examine the pathways that lead to youth

involvement in the juvenile justice system through this

lens.

Research consistently shows gender-related differences

in delinquent behavior. The literature suggests these

E. M. Espinosa (&) � M. A. Lopez Texas Institute for Excellence in Mental Health, Center

for Social Work Research, School of Social Work,

The University of Texas at Austin, 1717 West 6th Street,

Ste. 335, Austin, TX 78703, USA

e-mail: [email protected]

M. A. Lopez

e-mail: [email protected]

J. R. Sorensen

Department of Criminal Justice, East Carolina University,

Rivers Building, Office #245, Mail Stop 505, Greenville,

NC 27858, USA

e-mail: [email protected]

123

J Youth Adolescence (2013) 42:1824–1836

DOI 10.1007/s10964-013-9981-x

differences first emerge early in child development and

become more pervasive in adolescence. Some leaders in

criminology have suggested that gender differences in

delinquent behavior can be attributed to differential

socialization between genders (Bottcher 2001), while oth-

ers have argued that differences are tied to offender status

in a gender-stratified society (Chesney-Lind 2002). How-

ever, a third model emerges when examining both the

developmental criminological and the developmental psy-

chology literature. The developmental psychology litera-

ture has shown that females are more likely to exhibit

internalizing symptoms that may not come to the attention

of the adults in their life (Rosenfield et al. 2005), while

males are more likely to exhibit externalizing behaviors,

which are problematic for other people and society

(Compton et al. 2002; Kazdin 2005). Greater internalizing

results in girls having increased rates of depression, bipo-

lar, anxiety, post-traumatic stress, and other mood disor-

ders. Boys tend to have higher rates of conditions such as

attention-deficit/hyperactivity disorder, oppositional defi-

ant disorder, and conduct disorder. Therefore, one possible

explanation of the gender differences found in the

involvement of youth in the juvenile justice system could

be explained by differences in mental health conditions that

may develop and/or intensify in adolescence.

Gendered Pathways to Delinquency

Pathways toward and through the juvenile justice system

differ between girls and their male counterparts. This may

be related to how boys and girls develop their self-concepts

and identities. Boys’ self-concepts and identities are

developed in relationship to the world, while girls’ and

young women’s self-concepts and identities are developed

through their interactions with others (Gilligan and Brown

1992). Gilligan and Brown contend that female moral

development is based on a personal view and commitment

to others. Although female offenders occasionally engage

in conduct more stereotypical of males, such as aggression

and assaultive behavior, more often they suppress their

aggression and struggle with the difficulty of managing

their emotions, especially those associated with depression

and anxiety (Ford et al. 2006). Delinquent girls have a

higher risk of self-devaluation, suicidality (Wasserman

et al. 2005), and conflict with family and school compared

with their male counterparts (Zoccolillo et al. 1996).

Attachment, interdependence and connectedness are criti-

cal to the foundation of their identity.

Gender Disparity in System Processing

Studies of delinquency and the response of the juvenile

justice system have consistently found both legal and extra-

legal factors contribute to the detention and dispositional

outcomes of youth involved in juvenile offending. How-

ever, findings have been inconsistent regarding the effects

of gender on case outcomes in post-adjudication disposi-

tion decisions (Belknap and Holsinger 2006). Some studies

have revealed girls were the recipients of more severe

sanctions than their male counterparts, especially in

response to status offenses (Chesney-Lind 2002). Other

studies indicated females received more lenient outcomes

for delinquent behavior (class B misdemeanor and higher

offenses) than males. Some research discovered that out-

comes depend on the stage of processing. For instance,

MacDonald and Chesney-Lind (2001) reported no differ-

ence between boys and girls in the decision to petition an

offense. However, during the adjudication stage, ‘‘charge

seriousness’’ was more important for girls than boys with

the reverse trend during the disposition stage. Thus, when

female juvenile offenders were adjudicated delinquent,

they were ‘‘more likely than boys to be given a restrictive

sanction for a less serious offense’’ (p. 187).

It has become common knowledge in criminology that

by engaging in a practice referred to as bootstrapping,

courts detain females through findings of contempt of

court, probation violations, or violations of court orders for

underlying status offenses or minor delinquent behavior

(Sherman 2005). As a result of bootstrapping, early evi-

dence suggests more female juvenile offenders are detained

prior to adjudication for offenses less threatening to the

community than those of their male counterparts. Data

from the Juvenile Detention Alternatives Initiative (JDAI),

launched by the Annie E. Casey Foundation in 1992,

demonstrated the number of juveniles housed in secure

detention nationwide increased by 72 % between 1985 and

1995 (Sherman 2005). While it may be assumed this

increase reflected the need for community safety, less than

one-third of the juvenile offenders detained in 1995 were

charged with a violent offense. Across both genders, more

youth were detained for status offenses than violent

offenses, with violations of court orders accounting for

39.9 % of the detention population. This trend was even

greater for female juvenile offenders, who were more likely

than their male counterparts to be detained for status

offenses and technical violations (Sherman 2005). Similar

findings have been demonstrated in several study replica-

tions (American Bar Association and the National Bar

Association 2001; Sickmund et al. 2004).

For instance, Gavazzi et al. (2006) noted girls were more

likely to be detained for incorrigibility and domestic vio-

lence, and parents were more likely to be the complainants.

Their findings also indicated boys were more likely to be

arrested for property offenses, with complainants more

likely to be community citizens. The authors summarize

the difference between male and female juvenile detention

J Youth Adolescence (2013) 42:1824–1836 1825

123

decisions by stating that: ‘‘boys are detained as a response

to public safety issues, whereas girls are detained because

of problems at home’’ (p. 608). By 2003, this trend had

extended to custodial placements other than detention as

well, with females accounting for 40 % of the status

offenders but only 14 % of delinquents held in custody

(Snyder and Sickmund 2006).

Mental Health Disorders and Delinquency

Recent studies suggest a correlation between juvenile jus-

tice system processing and psychiatric disorders, with some

research indicating girls with mental health needs are

funneled deeper into the system for less serious offenses

than their male counterparts. Abram et al. (2003), in a

study of Cook County Juvenile Detention youth, found

females were 1.4 times more likely than males to meet

diagnostic criteria for at least one disorder, and they also

were more likely to have at least one co-morbid disorder.

Davis et al. (2009) discovered females receiving care in the

community mental health system were arrested at younger

ages and more frequently than girls not receiving public

mental health treatment. In addition, for those youth

needing hospitalizations, girls had shorter lengths of stay

than boys (Pavkov et al. 1997). These findings have lead

some researchers to suggest girls are typically undertreated

for their mental health needs and others to suggest this lack

of treatment results in their involvement in the juvenile

justice system (Wasserman et al. 2005).

Youth involved with the juvenile justice system often

have not one, but several co-morbid psychiatric disorders.

Wasserman et al. (2005) found the prevalence of youth

meeting criteria for at least one psychiatric disorder to be

39 %, with 16 % meeting criteria for three or more disor-

ders. In addition to the growing prevalence of youth with

mental health challenges in the juvenile justice system,

studies also indicate mental health disorders are correlated

with delinquent behavior. Several prospective studies

indicate hyperactivity (Lynam et al. 2000), conduct disor-

ders and emotional disorders (Copeland et al. 2007; Boots

2008; Boots and Wareham 2009) serve as key indicators

for involvement with the justice system. Specifically, Co-

peland et al. (2007) found 20.6 % of female juvenile

offending and 15.3 % of male juvenile offending was

attributable to mental health disorders, after controlling for

offense level and poverty. Among specific psychiatric

profiles, the findings indicate co-occurring anxiety and

depressive disorders had the strongest association with

delinquent behavior. Boots and Wareham (2009) extended

these findings further when they demonstrated a moderate

correlation between depression and anxiety (r = .577) and

future offending.

Trauma and Delinquency

Although not all youth who experience trauma engage in

delinquent activity, studies of youth involved with the

juvenile justice system have found high rates of trau-

matic experiences, generally between 70 and 90 %

(McMackin et al. 1998; Steiner et al. 1997). Some

studies have found boys and girls involved with the

juvenile justice system experienced different types of

traumas, with males more likely to have witnessed a

violent event and females more likely to have been the

victim of violence. The Survey of Youth in Residential

Placement, conducted on a sample of over 7,000 incar-

cerated youth, indicated females were almost twice as

likely to report prior physical abuse (42 % of females

versus 22 % of males), and females reported a higher

likelihood (69 % of females versus 40 % of their male

counterparts) of the perpetrator of the physical abuse

being a sibling or mother (Sedlak and McPherson 2010).

Researchers also found girls who reside in violent homes

have heightened risk factors for engaging in delinquent

activity, such as truancy, sexual promiscuity, running

away, and substance abuse (Thornberry et al. 2004). Not

surprisingly, female juveniles arrested for running away

frequently report experiences of family violence and

emotional, physical, and sexual abuse and report these

conditions as their primary motivation for leaving home

(Chesney-Lind 2002). Furthermore, studies indicate

females who have experienced trauma develop mental

health problems as a result of that trauma more often

than their male counterparts (Crimmins et al. 2000).

Some studies found girls who have mood disorders are

more likely to have experienced trauma and are more

likely to have post-traumatic stress disorder (Wasserman

et al. 2005).

Sexual victimization, in particular, is a common form

of trauma experienced by girls involved in the justice

system and is likely a contributing factor to the complex

mental health needs of this population. Although virtually

absent from formal theories of female delinquency, some

studies examined the correlation between sexual abuse

and female juvenile delinquency. In a study of chroni-

cally delinquent offenders, Sherman (2005) found 77 %

of female offenders had a history of sexual abuse in

comparison to only 3 % of the males, suggesting a

potential relationship between trauma and chronic delin-

quency in girls. Furthermore, Goodkind et al. (2006)

found juvenile justice-involved girls who have experi-

enced some form of sexual abuse had poorer mental

health and more substance use, risky sexual behavior, and

delinquent behavior than those who had not experienced

this form of trauma.

1826 J Youth Adolescence (2013) 42:1824–1836

123

Hypotheses

Despite the overall decline in the rate of juvenile delin-

quency, the involvement of girls with the juvenile justice

system has increased. In addition, there has been an

enhanced recognition of the disproportionate representation

of youth with mental health needs and trauma histories in

the juvenile justice system. While males typically are

associated with externalizing problems, females dispro-

portionately are identified with internalizing problems and

interpersonal aggression. This changing demographic of

juvenile delinquency poses challenges to the traditional

juvenile justice system accustomed to handling behaviors

associated with externalizing manifestations of delin-

quency. Analyzing the extent to which mental health need,

trauma, and gender influence juvenile justice system pro-

cessing will provide a more comprehensive understanding

of the pathways youth take in the juvenile justice system,

as well as identify potential modifications needed to

address the unique needs of youth accessing the system in

the future.

First, we hypothesized that more girls would be placed

outside of the home for bootstrap level offenses, such as

status offenses and violation of probation, than boys. This

analysis sought to determine whether girls are funneled

deeper into the system for lower level offenses than their

male counterparts. Second, we hypothesized that greater

mental health need, as measured by the mental health

screening instrument, would be associated with a greater

risk of out-of-home placement. Finally, we formed an

exploratory hypothesis aimed toward examining the influ-

ence of the endorsement of prior traumatic experiences, as

measured by the mental health screening instrument, on the

restrictiveness of out-of-home placement decisions. We

were interested in whether a trauma history increased the

likelihood of a juvenile being removed from their home. In

addition to these three primary hypotheses, a secondary

analysis explored the relative importance of these variables

on different types of out-of-home placement.

Method

The study sample included all youth referred to three urban

juvenile probation departments in Texas during the period

of January 1, 2007 through December 31, 2008. Only youth

who received the state-mandated mental health screening,

the Massachusetts Youth Screening Instrument-Second

Version (MAYSI-2) were included (N = 34,222; 30.1 %

female). This secondary dataset included all demographic,

offense, disposition and placement data collected by

trained juvenile probation officers and clinicians within the

departments. Data was obtained with approval from the

Chief Juvenile Probation Officer and the juvenile board and

the protocol was reviewed and approved by the Institu-

tional Review Board at the researchers’ university.

Measures

The main predictor variables considered were referral

offense seriousness, gender, and level of mental health

need. Gender is a dichotomous static variable and was

coded as male (0) or female (1) for analysis. The referral

offense seriousness and level of mental health need vari-

ables could be interpreted with a broad array of values;

therefore, specific operational definitions and categorical

values for these variables were developed prior to con-

ducting analyses.

Offense Seriousness

Youth could have been referred to local juvenile probation

departments for multiple offenses on any one referral

event. Therefore, this study targeted the referral offense

associated with most severe disposition during the sample

period. The categorical coding guidelines identified within

the Texas Juvenile Probation Commission (TJPC) data

codebook were used for establishing operational definitions

and assigning categorical values for offense seriousness.

The TJPC data codebook is used by juvenile probation

officers collecting and entering data into the state’s data

collection system. This coding process categorized the

4,019 types of offenses into a continuous classification

variable ranging from the least serious 1 (status offenses) to

the most severe 8 (capital felony). The classifications

between status offense and capital felony included the

following: 2 = Class B misdemeanor; 3 = Class A mis-

demeanor; 4 = State-jail felony; 5 = Third-degree felony;

6 = Second-degree felony; and 7 = First-degree felony.

In addition to the ordinal offense severity code, two

dichotomous indicator variables were constructed to eval-

uate the potential bootstrapping of juveniles into and

through the juvenile justice system. Bootstrapping has been

defined in the literature as engaging in a practice whereby

courts detain females through findings of contempt of

court, probation violations, or violations of court orders for

underlying status offenses or minor delinquent behavior

(Sherman 2005). A traditional bootstrap variable (Status)

included offenses that have been typically categorized as

status offenses (otherwise known as ‘‘Conduct Indicating

Need for Supervision Offenses’’ or CHINS offenses), Class

C misdemeanors, and contempt of court referrals. These

types of offenses include runaway, truancy, and curfew

violations. Class C misdemeanors are typically violations

of city or county ordinances and are processed in a manner

similar to status offenses. A second bootstrap variable

J Youth Adolescence (2013) 42:1824–1836 1827

123

(VOP) included violations of probation or juvenile court

order.

Mental Health Need

Texas has adopted the MAYSI-2 for mental health

screening within the juvenile justice system (Grisso 2004;

Schwank et al. 2003). The MAYSI-2 is a 52-item, self-

report screening instrument completed by youth between

the ages of 12 and 17 upon intake in the juvenile justice

system. The MAYSI-2 contains seven factor-analytically

derived subscales: Alcohol and Drug Use, Angry-Irritable,

Depressed-Anxious, Somatic Complaints, Suicidal Idea-

tion, Thought Disturbance, and Traumatic Experiences.

Studies have demonstrated good concurrent validity when

comparing MAYSI-2 scales with scores on other mental

health measures (Archer et al. 2004; Grisso and Barnum

2006). Test–retest reliability up to eight days later was

moderate to good, ranging from 0.53 to 0.89 (Grisso and

Barnum 2006). Cut-off scores for the MAYSI-2 subscales

(excluding Traumatic Experiences) were developed to

identify youth scoring greater than 90 % of the normative

sample on each subscale (Grisso and Barnum 2006).

Overall mental health need was defined as the total number

of subscales reaching this ‘‘warning’’ cut-off, ranging from

0 to 6.

The traumatic experiences subscale of the MAYSI-2

does not have established warning cut-offs, and so was kept

in its original reporting format, with a scoring range of 0–5.

Four questions on the subscale are common to both gen-

ders: ‘‘Have you been badly hurt or been in danger of

getting badly hurt or killed?’’, ‘‘Have you ever in your

whole life had something very bad or terrifying happen to

you?’’, ‘‘Have you ever seen someone severely injured or

killed?’’, and ‘‘Have you had a lot of bad thoughts or

dreams about a bad or scary event that happened to you?’’.

For boys, the fifth question is ‘‘Have people talked about

you a lot when you’re not there?’’. For girls, this fifth

question is ‘‘Have you ever been raped or been in danger of

getting raped?’’.

Level of Placement

Level of placement was categorized into a five-point

ordinal variable, with higher scores on the scale repre-

senting more ‘‘severe’’ placements. Categorization reflec-

ted not only the determination of whether the facility was

secure or non-secure, but also consideration of what

intercept point in the juvenile justice system (pre or post

disposition) the juvenile could be placed within the facility.

No placement is reflected by a ‘‘0’’ on the scale and

detention is reflected by a ‘‘1’’. Although often a secure

setting, juvenile detention facilities are the first type of

facility within the juvenile justice system a youth can be

placed and are frequently used to hold juveniles while

awaiting court decisions pre-adjudication. The next three

levels of placement severity within the composite were

non-secure (2), county secure (3), and state correctional

facility (4). Non-secure facilities included facilities

licensed by the state child welfare agency to provide foster

care or treatment services and included residential treat-

ment centers, emergency shelters, substance abuse treat-

ment facilities, therapeutic camps, and foster care. Local

juvenile probation departments contract with non-secure

facilities to care for juveniles who are considered low risk

and in need of some form of treatment or other basic care

needs. County secure facilities included county-operated

secure post-adjudication programs registered with the

state’s juvenile justice department to serve as an interme-

diate placement option for moderate or high-risk juveniles.

These facilities may include juveniles who have been

adjudicated of either misdemeanor or felony offenses. State

correctional facilities included high security facilities

intended for high-risk juveniles with felony adjudications

and represent the state’s youth prison system (Texas

Juvenile Justice Department 2012).

Control Variables

Control variables included age at first referral, age at target

referral, ethnicity, severity of offense history and prior

probation referrals. Ethnicity was reflected by dummy

coding two variables—Hispanic (1) or White (0) and Black

(1) or White (0). Both age at first referral and age at target

referral were also included in the analyses. Almost half of

the juveniles in the sample had a prior record with the

partnering juvenile probation departments (n = 16,077).

Severity of offense history was categorized using the same

procedures as the target referral offense. The seriousness of

offense history ranged from 0, indicating no prior record, to

8, indicating prior referral for a capital murder. Prior pro-

bation referrals were defined as the number of prior dis-

positions to probation supervision.

Procedures

Differences in variables of interest and control variables by

gender were examined through independent t-tests and Chi

square analyses. Multivariate analyses examined level of

placement by gender and mental health need. First, the

analyses sought to establish the general influence of mental

health need and gender on level of placement. The exam-

ination of the general influence of the predictor variables

on level of placement for all facility types utilized an OLS

regression model with gender (0 = male, 1 = female)

included in the equation, along with other predictor

1828 J Youth Adolescence (2013) 42:1824–1836

123

variables, regressed on facility composite (0 = no place-

ment, 1 = detention, 2 = non-secure, 3 = secure, 4 =

corrections). The OLS regression model is presented for

ease of interpretation. Results from ordinal logistic

regression and probit models confirmed the significance,

direction, and relative magnitude of coefficients presented

in the OLS model.

Additional analyses examined the influence of the pre-

dictor variables by specific facility type and gender at both

the pre-adjudicatory and post-adjudicatory phases, requir-

ing separate female and male models. A dichotomous

outcome variable was created for the pre-adjudicatory

detention decision and coded as either not detained (0) or

detained (1). Binary logistic regression was used to model

the detention decision. Multinomial logistic regression was

used to model post-adjudicatory placement, which includes

separate panels to describe the influence of predictors on

placement in non-secure facilities, county operated secure

facilities, and state correctional facilities. Alternatives to

placement served as the reference category for the multi-

nomial comparisons. Detention was included as a predictor

variable in the post-adjudicatory placement model. Status

offense had to be removed from the post-adjudicatory

placement model. Limited cell size prevented model con-

vergence when status offense was included. Additional

analysis was conducted to test for differences between

regression coefficients of the gendered models. The for-

mula suggested by Brame et al. (1998) was used to test for

differences between the model coefficients:

Z ¼ b1 � b2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

SEb21 þ SEb22 p

Results

Table 1 presents outcomes, offense seriousness, mental

health need, and control variables by gender. The mean

facility composites show boys are placed deeper in the

system overall. The binary outcomes show males are more

likely to be placed in each type of confinement, with the

exception of non-secure placement, where girls are twice as

likely to be placed. Part of the reason is apparent in the

Table 1 Level of placement, offense seriousness, mental

health need, and control

variables by gender

* p \ .05; ** p \ .01; *** p \ .001

Female

Mean (SD)

Male

Mean (SD)

Test of difference

Level of placement

Facility composite 0.496 (0.748) 1.024 (1.212) t = -49.130***

Detention 0.373 0.531 v2 = 725.656***

Non-secure placement 0.068 0.033 v2 = 213.268***

County secure placement 0.013 0.142 v2 = 1293.434***

State correctional commitment 0.008 0.048 v2 = 332.220***

Offense seriousness

Offense composite 2.091 (1.956) 2.789 (1.641) t = -48.709***

Status offense (Boot1) 0.144 0.068 v2 = 512.619***

VOP (Boot2) 0.089 0.143 v2 = 192.406***

Mental health need

Warnings (total #) 0.284 (0.697) 0.232 (0.688) t = 6.400***

Drug 0.023 0.020 v2 = 4.854*

Angry 0.109 0.068 v2 = 166.115***

Depressed 0.104 0.047 v2 = 385.650***

Somatic 0.065 0.036 v2 = 143.961***

Suicide 0.141 0.059 v2 = 628.816***

Thought 0.164 0.113 v2 = 94.904***

Trauma score 1.1300 (1.470) 1.120 (1.232) t = 11.182***

Control variables

Race–Black 0.363 0.373 v2 = 3.386

Ethnicity–Hispanic 0.390 0.413 v2 = 15.993***

Age at referral 14.900 (1.275) 14.956 (1.387) t = -3.581***

Age of onset 14.383 (1.399) 14.140 (1.560) t = 14.225***

Severity of offense history 0.894 (1.508) 1.691 (2.163) t = -39.075***

Prior probated dispositions 0.208 (0.560) 0.455 (0.830) t = -32.108***

Prior facility composite 0.205 (0.536) 0.521 (0.985) t = -38.208***

J Youth Adolescence (2013) 42:1824–1836 1829

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level of offense, which shows that girls, on average,

commit less serious offenses and are more than twice as

likely as boys to be referred for status offenses. Girls also

had less serious prior records, as evidenced by the control

variables. They also tended to be …