Gender and Programming

Abstract

The current study examines U.S. prison programming availability and participation by gender on a national level. The authors build upon previous literature by using national-level data, something that has been done in very limited cases previously. The main concern of this study is gender and its effects on programming availability and participation. The U.S. corrections field has undergone major changes in regard to population trends, fiscal constraints, policies, and research over the last few decades without a large-scale examination of the effects of these changes on programming across the United States. In this study, multiple types of programming areas were examined and results indicated that often female prisons (i.e., prisons housing only females) were more likely to offer programs (e.g., mental health options) and women were more likely to participate in many programming options compared with male prisons and men, respectively. We discuss the possible reasons for this and implications for future research.

Keywords

corrections women in prison prison programming

Beginning in the 1980s, criminologists became increasingly interested in U.S. correctional programming for women offenders. Women offenders have been historically neglected in the corrections system because of their relatively small numbers, and the limited programming (i.e., educational and vocational) that they were given was either copied from the larger male prison system or heavily influenced by traditional gender stereotypes (Belknap, 2010; Rafter, 1990). More recent scholars have focused on the types of programs offered to incarcerated men and women, yet many of these studies either focused on one particular type of programming area (e.g., mental health programming only) or several types of programs for a limited number of inmates (e.g., at only one facility). We are aware of only one national study on prison programming in the United States; however, it was completed several decades ago (e.g., Morash, Haarr, & Rucker, 1994). Thus, although there has been a focus on prison programs for women, it has not been done on a large scale in the United States. Moreover during the past 20 to 30 years, state correctional systems in the United States have undergone considerable changes in terms of budgets, with scaled back programs, and a shift in focus from rehabilitation to deterrence and incapacitation, which is another reason why it is important to consider how programming in prisons has changed since the 1980s (Lillis, 1994; Phelps, 2011).

In the correctional system, there has been a mass increase of incarcerated offenders, an influx of literature examining those offenders and the programs provided to them while incarcerated, alongside fluctuations and constraints placed on penal budgets due to economic hardships. Even though more recent population trends have demonstrated a decline in numbers (Glaze, 2010), the growth of inmates across the United States has been exponential, particularly for incarcerated women, whose population has grown at a much larger rate than that for men (Belknap, 2010). This growth, in turn, has significantly affected the correctional system in regard to management, programming, and budget issues (Glaze, 2010; Porter, 2011). The corrections system has also experienced fundamental changes as a result of a major U.S. economic recession over the last several years. The recession and subsequent budgetary restrictions came at a time when the costs of running a prison system have steeply risen due to the mass incarceration of the inmate population (Henrichson & Delaney, 2012). Thus, situated within the recent economic downturn in the United States, prisons across the United States adopted cost-cutting measures, some of which included cutting funding for programs (Kruttschnitt & Gartner, 2003; Lillis, 1994; Porter, 2011). For instance, in a survey of U.S. state prison systems, Lillis (1994) found that about 50% of respondents reported cutting funds to vocational and technical training, and educational programs during the early 1990s. The result of these cuts led to larger classes, a longer waiting time to participate in programs for inmates, and even less programs being offered to them (Lillis, 1994). In addition, research studying inmates and prison operations, including programming, has significantly increased in the last few decades, somewhat in tandem with the growth of prisoners.

The influx of correctional research in the United States generally focused on examining empirical-based practices so that research would inform policies at both the local and state levels. In the correctional research on females, the literature focused on gender-specific or gender-responsive programs and policies. This perspective holds that although there are many similarities between male and female offenders (i.e., family criminal histories, drug abuse, trauma), there are important differences regarding background characteristics and criminal tendencies (Daly, 1992; Huebner, DeJong, & Cobbina, 2010; Salisbury & Van Voorhis, 2009), and as a result, men and women may have different programming needs (Bloom, Owen, & Covington, 2005). Thus, the increased attention to programming, and more specifically gender-responsive approaches, may have resulted in tangible changes in correctional programming (e.g., types) during the last several decades as to what works for prisoners, and what works for female prisoners in particular.

It is unclear how these factors may have influenced the provision of and participation in correctional programming for male and female inmates given the considerable changes in the corrections area. The current article contributes to the literature by conducting a review of the availability of prison-based correctional programming and levels of participation for men and women across the United States. This review is particularly relevant as it is now more than 20 years since a similar review was undertaken (e.g., Morash et al., 1994).

Prison Programming

Historically, the role and significance of rehabilitation, or an attempt to redeem offenders and reintegrate them into society, and the use of prison programs within the U.S. correctional system have varied depending on the importance of religion, views on human nature and particular types of criminals, support for punishment, empirical evidence, and program efficacy (Cullen & Jonson, 2011; Ignatieff, 1981; Kirchhoff, 2010; Meskell, 1999). For instance, the first rehabilitation programs in U.S. prisons involved hard labor and silent contemplation (Ignatieff, 1981; Kirchhoff, 2010; Meskell, 1999). Rehabilitative approaches progressed through the years from focusing on penitent programs to a concern with physical and mental health along with addressing inmates’ lack of legitimate work skills (Goodstein & MacKenzie, 1989). More recently, inmates have been treated through a variety of programming options including, educational, vocational, medical and mental health, and substance abuse programming (Cullen & Jonson, 2011). It is difficult to gauge the level of participation in prison programming and its availability to inmates across the United States due to a lack of nation-wide empirical investigation. Much of the research on prison programming in the United States has focused on two main themes: needed programming for inmates (i.e., programs to aid in rehabilitation) and evaluations of existing programs (see, for example, Andrews, Bonta, & Hoge, 1990; Cullen & Gendreau, 1989; Cullen & Jonson, 2011; Cullen, Smith, Lowenkamp, & Latessa, 2009; French & Gendreau, 2006; MacKenzie, 2000). Furthermore, existing research typically examines programming within one prison or state system (e.g., Department of Corrections Reports and Assessments), or it focuses only on one type of programming nationally (e.g., mental health; James & Glaze, 2006), with the exception being a study conducted by Morash et al. (1994), but published more than two decades ago.

In response to the increased attention on female prisoners at the time, Morash and her colleagues (1994) conducted a study comparing prison programming for men and women in the United States using data from the mid-1980s. As part of their study, they used data from the Census of State and Adult Correctional Facilities, 1984, and the Survey of Inmates in State and Federal Prisons, 1986, and made comparisons between male and female inmates in terms of availability and participation in programs across the country. They also considered the potential influence of other individual-level and facility-level characteristics including prison size, location, and security level on programming (Morash et al., 1994).

Morash and her colleagues (1994) found that for many programming options, women participated in more programs and women’s prisons offered more programs. For example, women were 20% more likely than men to participate in educational programs, and a higher proportion of women’s prisons offered educational programs than men’s prisons, which they stipulated might have been due to court orders that had been previously sought to provide more equitable programming for women. Women were also twice as likely to be given psychotropic medications even when controlling for other explanatory factors such as prior hospital and drug admissions (Morash et al., 1994). Yet, even though a similar proportion of men and women reported receiving a drug treatment program, women were 15% less likely to participate in drug treatment (Morash et al., 1994). They also considered parenting programs and found that these classes and programs were almost exclusively used in women’s facilities, thereby continuing a long tradition of socializing women to be good mothers and keep a home (Franklin, 2008; Lee, 2000; Morash & Robinson, 2002; Rafter, 1990), and they estimated that more women than men participated in these programs. Notably, they also found that programming availability and participation were influenced by several control measures involving facility characteristics such as location, size, and security level (Morash et al., 1994).

The 1994 study by Morash and her colleagues addressed vital research questions that continue to be relevant today as we consider whether or not male and female inmates are exposed to different types of prison programs and, more importantly, whether they are participating in prison programming at different levels. Since Morash and her colleagues (1994) completed their study, the correctional system in the United States has changed significantly. Over the last few decades, there have been an unprecedented number of women offenders entering prison, mainly for drug and other non-violent offenses (Belknap, 2003; Covington & Bloom, 2006; Davis & Shaylor, 2001). These women are entering a prison system that has been mainly “designed by men to punish and treat other men” (Franklin, 2008, p. 341). Consequently, it appears that their treatment and programming options are generally unsuited to what they need (e.g., viable work skills and therapeutic programming for recovery from alcohol and/or substance abuse and physical and sexual abuse), and still tend to endorse more stereotypical views of how women should be treated and the skill-sets of what they require (e.g., parenting classes and domestic skills; Franklin, 2008; Lee, 2000; Morash & Robinson, 2002; Rafter, 1990). In addition, more current research supports gender-responsive programming such as life-skills and therapeutic-based programming that can address the array of needs women in prison typically have (i.e., histories of victimization and substance abuse; Bloom et al., 2005).

As noted, the past several years have also been marked as a time of economic cutbacks and budgetary concerns due to global economic difficulties, which affected the type and amount of programming offered to inmates. With this in mind, the current study explores possible differences between male and female prisons in terms of available programming, and differences in participation for male and female inmates across a wide range of programming areas such as medical, mental health, substance abuse treatments, educational, vocational, and life skills. The current study, while not making a direct assessment of whether these corrections programs are gender responsive or evidence based, has included these program areas because of the prior literature on inmates’ needs, particularly females (Belknap, 2003; Bloom et al., 2005; Koons-Witt, Burrow, Morash, & Bynum, 1997). A national-level study of programming in prisons can highlight possible changes to programming in the United States, and may underscore an important recognition among corrections officials that certain types of programming are needed at higher levels for women compared with men. Accordingly, we are concerned with possible differences in programming options and availability in U.S. prisons based on gender. This study is guided by two research questions:

Research Question 1: Does gender of inmates housed in the facility influence program availability, controlling for other facility-level characteristics (e.g., size, security level, and location)?

Research Question 2: Are there notable differences in program participation rates for incarcerated men and women, controlling for other individual- and facility-level characteristics?

We address the first question by comparing male prisons with female prisons in terms of amount of available programming (i.e., low, medium, and high) for different types of program areas (i.e., mental health, life skills). In addition, we consider participation in more specialized programming areas such as the provision of psychotropic medications, payment for prison work assignments, and participating in parenting programs, which were identified in previous research as indicators of stereotypical treatment (Morash et al., 1994). This study is meant to provide criminologists and correction practitioners with a contemporary picture of current practice in U.S. state prisons for women and men, thus filling an important void in the existing corrections literature.

Method and Data

Two separate but related data sets were used to examine availability and participation. The 2000 Census of State and Federal Adult Correctional Facilities (U.S. Department of Justice, Bureau of Justice Statistics, 2000) provided us with an opportunity to compare program availability between prisons for men and women across the United States. The 2000 Census is not the most current enumeration, yet it was the most logical to use because it served as the sampling frame for the Survey of Inmates in State and Federal Correctional Facilities, 2004, which was also used in this study. The 2000 Census collected information from 1,600 facilities through a survey questionnaire mailed by the U.S. Census Bureau to prison administrators. The Census Bureau conducted follow-up mailings and phone calls until they achieved a 100% response rate.¹ Given the current research is interested in understanding programming in state prisons, federal prisons were excluded due to the potential for both measured and unmeasured differences between state and federal prisons, which might cause biased results (i.e., potential differences due to varying legal jurisdictions, offenses, etc.). Community-based facilities—facilities where 50% or more of the residents were regularly allowed to leave the facility unsupervised—were also excluded. Due to the specificity of the research questions on gender differences, coed facilities were also eliminated from the sample. Finally, a unique facility variable was created in the 2000 Census to provide a link to the Survey of Inmates in State and Federal Correctional Facilities, 2004. The analysis involved several types of Census data including inmate and facility characteristics, medical and mental health treatment, and program availability.

Using data from the 2004 Survey of Inmates in State and Federal Correctional Facilities (U.S. Department of Justice, Bureau of Justice Statistics, 2004), we were able to make a gender comparison for inmates’ participation in programming throughout U.S. prisons for several programming areas: medical care, mental health services, drug and alcohol treatment programs, work assignments, vocational training, education programming, and parenting/child-rearing programs. The sample for the survey was selected through a two-stage process for both state and federal prisons; again, however, the focus of the current study was interested in using information solely from state prisons. The first stage selected correctional facilities whereas the second stage selected inmates from those facilities.² This sampling approach resulted in a total sample of 287 facilities and 16,152 inmates (13,098 male and 3,054 female). Interviews that collected information on each inmate’s individual characteristics and background, prior treatments, and prison life were conducted between October 2003 and May 2004, lasted approximately 1 hr, and were conducted using computer-assisted personal interviewing.³

Program Availability Measures

Dependent variables

The current study considered available programs in the following areas: medical and mental health care, work assignments, educational and vocational programs, and life-skills programming. The first step was to determine whether or not prisons provided specific types of programming or services within each of the program areas or domains (see Appendix Table A1 for detailed results). The Census collected information for seven specific types of services or treatments in the medical program domain; six specialized programs in each for the mental health, work, and education program domains; and five types of programming in the life-skills program domain (see Appendix Table A2 for descriptive results for each program domain scale). Next, we summed the number of specific services or programs to obtain an overall scale score for each program domain and then used the distribution of scores to create an ordinal scale that classified each prison as having a low, medium, or high level of programs or services for each area or domain. This classification was completed by examining the distributions and making cut-offs as close to the 33 percentile as possible for each program domain distribution. For example, for the medical care domain, low was computed as scores 0 to 5 (27.5%), medium was a score of 6 (33.4%), and high was a score of 7 (37.8%). All other outcomes were classified similarly as detailed in Table 1.

Table 1.

Descriptives for Level of Available Programs by Area (Census).

Program area	Low (f)	Medium (f)	High (f)
Medical care	26.7% (277)	33.4% (364)	37.8% (392)
Mental health care	29.7% (308)	21.9% (227)	48.4% (502)
Work assignments	46.1% (478)	31.7% (329)	21.1% (219)
Educational programming	24.8% (257)	29.3% (304)	45.9% (476)
Life-skills programming	39.3% (408)	24.0% (249)	36.6% (380)

Note. N = 1,037 prisons.

Independent and control variables

The main independent variable of interest for program availability was whether the prison housed male or female inmates. In addition, other facility-level characteristics were examined for their influence on the likelihood that particular programs are offered to inmates in certain types of prisons: location, security, and size. As expected, a clear majority of the facilities housed incarcerated men (91%) as shown in Table 2. In regard to the facility-level characteristics, location was computed to indicate the area of the country where the prison was located using U.S. Census Bureau regional designations (U.S. Census Bureau, 2015), and states were assigned to the Northeast (16%), South (48%), Midwest (22%), or the West (15%) region (the reference group). Security level was measured as maximum/supermax, medium, and minimum (the reference group). The largest percentage of facilities was identified as medium-level security (42%), followed by minimum level (29%), and maximum level (29%). Facility size was measured using ratio values, and on publication of the Census, prisons housed an average of 1,028 inmates (Table 2). For more specific information about male and female facilities in relation to the independent and control variables, please see Table 2.

Table 2.

Descriptives for Independent Variables for Program Availability (Census).

	Full sample	Males	Females
Variable	% or M	% or M	% or M	Description
Female prison	9.1% (94)			0 = no (male); 1 = yes
Location—region
West	14.6% (151)	14.1% (133)	19.1% (18)	0 = other; 1 = West
Midwest	21.6% (224)	21.4% (202)	23.4% (22)	0 = other; 1 = Midwest
South	47.7% (495)	48.1% (454)	43.6% (41)	0 = other; 1 = South
Northeast	16.1% (167)	16.3% (154)	13.8% (13)	0 = other; 1 = Northeast
Facility security level
Supermax/max	28.6% (297)	28.1% (265)	34.0% (32)	0 = other; 1 = supermax/max
Medium	42.1% (437)	42.7% (403)	36.2% (34)	0 = other; 1 = medium
Minimum	29.2% (303)	29.2% (275)	29.8% (28)	0 = other; 1 = minimum
Facility size	1,027.58 (969.92)	1,067.33 (989.80)	638.88 (616.13)	Size of facility (6-7,223)

Note. N = 1,037 prisons.

Program Participation Measures

Dependent variables

For program participation by inmates, the current study focused on the following areas: mental health, substance abuse, education, vocational training, life skills, and work. The survey collected information on specific treatments, services, and programs within the above-mentioned program areas or domains. Similar to the Census data, we created aggregate program domains from these more narrowly focused programs or services. Program participation was measured as a dichotomous variable indicating whether or not the inmate participated (yes or no) in any programming within the particular program domain (see Appendix Table A3 for descriptive information on specific services and programs). Approximately, 19% of all inmates (17.6% of male and 39.6% of female) received some type of mental health assistance or care, whereas almost 37% of all inmates (36.1% of males, 41.2% of females) participated in some form of substance abuse treatment (i.e., detoxification units, inpatient drug treatment, outpatient treatment or counseling, self-help group/peer counseling, education/awareness, maintenance program, and other) as shown in Table 3. Two of three inmates reported that they had some type of work assignment while in prison (either on or off prison grounds) and almost 28% received some type of vocational training. By gender, approximately 66% of males and 70% of females had a work assignment, whereas around 28% of men and 26% of women had vocational training.

Table 3.

Descriptives for Participation by Program Domain and Select Targeted Areas (Survey).

	Full sample—yes	Male—yes	Female—yes
Program area—dependent variables	% (f)	% (f)	% (f)
Mental health care	19.1 (2,717)	17.6 (2,335)	39.6 (382)
Psychotropic medications	15.1 (2,157)	13.8 (1,841)	32.7 (316)
Substance abuse programming	36.5 (4,879)	36.1 (4,512)	41.2 (367)
Educational programming	31.5 (4,498)	31.4 (4,175)	33.5 (323)
Vocational training	27.5 (3,799)	27.6 (3,558)	25.8 (241)
Work assignments	66.2 (9,462)	65.9 (8,784)	70.3 (678)
Paid for work	38.1 (5,433)	38.0 (5,056)	39.1 (377)
Life-skills programming	29.3 (4,178)	28.5 (3,794)	39.9 (384)
Parenting	8.3 (1,150)	7.5 (967)	19.6 (183)

Note. N = 14,499 inmates; 1 = yes and 0 = no.

Finally, one in three obtained educational programming of some sort (e.g., basic education, General Education Diploma (GED), English as a Second Language (ESL), college courses), and about 29% of inmates participated in life-skills programming (e.g., employment counseling, parenting, and pre-release) during their imprisonment (Table 3). Approximately 31% of men and 34% of women participated in educational programming, whereas almost 29% of men and 40% of women participated in life-skills programming. We also included dichotomous outcome measures for several more targeted programs including the receiving of psychotropic medications (15.1% all, 13.8% males, 32.7% females), being paid for work during imprisonment (38.1% all, 38.0% males, 39.1% females), and participating in parenting or child-rearing programs (8.3% all, 7.5% males, 19.6% females) to examine whether there have been any changes in programs more traditionally indicative of stereotypical programming.

Independent and control variables

The main independent variable of interest for participation in prison programming was inmate gender. In parity with national prison statistics, the vast majority of inmates were male (93%) as shown in Table 4. Other individual-level factors examined were race, age, personal history information, criminal history, time served, and rule violations. Race was measured using inmates’ self-report of White, Black, or “Other.” The largest numbers of inmates were White (46%). Notably, a larger number of women (53.4%) than men (45.4%) were White. Age was a continuous variable that measured the inmates’ age in years with the average age being 35 years; this was true for the full sample and for both genders.

Table 4.

Descriptives for Independent Variable Measures for Program Participation (Survey).

	Full sample	Males	Females
Variable	% or M	% or M	% or M	Description
Female	6.8% (951)			0 = no (male); 1 = yes
Race
White	46.0% (6,667)	45.4% (6,143)	53.4% (525)	0 = no; 1 = White
Black	41.4% (6,004)	41.9% (5,663)	34.7% (341)	0 = no; 1 = Black
Other	12.6% (1,828)	12.7% (1,711)	11.9% (117)	0 = no; 1 = Other
Age	35.36 (10.60)	35.34 (10.69)	35.55 (9.30)	age in years (16-84)
Dependent children	58.3% (8,356)	57.6 (7,703)	66.5% (653)	0 = no; 1 = yes
Drug dependency	3.18 (4.14)	3.08 (4.09)	4.48 (4.62)	Self-assessed score (0-11)
Alcohol dependency	2.30 (3.36)	2.30 (3.35)	2.29 (3.46)	self-assessed score (0-11)
Sexual abuse	8.7% (1,241)	6.2% (833)	42.4% (408)	0 = no; 1 = yes
Physical abuse	16.2% (2,325)	13.6% (1,844)	49.6% (480)	0 = no; 1 = yes
Employment history	72.4% (10,177)	73.5% (9,618)	58.2% (559)	0 = unemployed prior; 1 = employed
Education
>High school	11.7% (1,688)	11.7% (1,574)	11.8% (115)	0 = other; 1 = less than high school
High school	74.0% (10,640)	74.4% (9,968)	69.1% (672)	0 = other; 1 = high school
Some or more college	13.9% (1,996)	13.5% (1,812)	18.9% (184)	0 = other; 1 = some or more college
(continued)
Mental health history	25.4% (3,603)	23.7% (3,132)	49.1% (471)	0 = no; 1 = yes
Mandatory drug/alcohol treatment	16.6% (2,343)	16.1% (2,125)	22.7% (218)	0 = no; 1 = mandatory treatment
Criminal history	1.56 (2.94)	1.59 (2.94)	1.21 (2.92)	No. of prior incarcerations (0-95)
Current offense
Violent offense	47.6% (6,805)	48.2% (6,517)	29.6% (288)	0 = other; 1 = violent
Property offense	19.1% (2,731)	18.3% (2,447)	29.1% (283)	0 = other; 1 = property
Drug offense	21.7% (3,109)	21.0% (2,805)	31.3% (304)	0 = other; 1 = drug
Other offense	11.6% (1,666)	11.8% (1,568)	9.9% (97)	0 = violent, property or drug; 1 = other
Time served	49.39 (61.91)	51.02 (62.85)	27.54 (41.84)	Time served in no. of months (0-526)
Rule violations	51.3% (7,309)	51.8% (6,875)	45.2% (434)	0 = none; 1 = one or more violations
Facility security level
Supermax/max	34.6% (5,016)	34.2% (4,629)	39.4% (387)	0 = other; 1 = supermax/maximum
Medium	49.7% (7,202)	50.4% (6,812)	39.6% (389)	0 = other; 1 = medium
Minimum	14.6% (2,121)	14.2% (1,918)	20.7% (203)	0 = other; 1 = minimum
Facility location
West	23.1% (3,343)	22.9% (3,098)	24.9% (245)	0 = other; 1 = West
Midwest	20.4% (2,957)	20.5% (2,774)	18.6% (183)	0 = other; 1 = Midwest
South	41.8% (6,056)	41.5% (5,608)	45.6% (448)	0 = other; 1 = South
Northeast	13.7% (1,983)	13.9% (1,880)	10.5% (103)	0 = other; 1 = Northeast
Facility size	1,938.30 (1,547.46)	1,984.52 (1,565.43)	1,302.30 (1,091.92)

Note. N = 14,499 inmates.

Several additional control variables were used in certain analyses to reflect the personal history of each inmate. These control measures included having dependent children (below 18 years), an alcohol and drug dependency (each measured using an 11-point scale), having experienced prior physical or sexual abuse (yes or no), a mental health history (measured through self-reports of any previous mental health diagnosis), education (less than high school, high school, and some or more college), and employment history (yes or no employed prior to prison). In addition, information about the inmates’ criminal history (no. of prior incarcerations) and current offense (violent, property, drug, and other), time served (in months), rules violations (yes or no), along with facility-level characteristics (security level, location, and size), which were taken from the 2000 Census data set and linked to the survey data, were used as control measures (see Table 4).

Findings

For the first research question, we used ordered logistic regression (OLR) to examine the relationship between whether the prison housed male or female offenders and the level of services or programming for each program domain.⁴ Because the OLR models for medical programming, mental health care, work assignments, and educational programming failed to meet the “proportional odds assumption” (POA)⁵ required for OLR as determined through the Brant test of parallel lines, these programs were analyzed using the generalized ordered logit model (GOLM). This approach is a suitable alternative for OLR because it relaxes the POA (see Fu, 1998; Williams, 2006). For each of these models, facility size, security, and location served as control measures.

As shown in Table 5, regarding gender, facilities housing women compared with men had significantly higher odds of offering high, versus medium or low levels of medical care, mental health care, work assignments, and education programs, and significantly higher odds of having medium or high levels of these programs available versus a low level of programming. Meaning, women’s facilities offered significantly more types of programs in these areas than did male facilities. For instance, compared with male facilities, female facilities had increased odds of 94% of having higher levels of medical care available versus lower levels. Facilities housing female inmates also had increased odds of 131% compared with those housing males of having higher levels of mental health care versus a low level of this programming. Work assignments and education programs were also significantly affected by whether the prison housed males or females. Compared with prisons for men, prisons for women had 84% increased odds for work assignments and 169% increased odds for educational programs of offering higher levels of programming compared with lower levels of programming. Many of the facility-level characteristics were also significant such as size and security level as shown in Table 5.

Table 5.

Proportional Odds Models for Program Area Availability Levels (Census).

	Medical care model		Mental health model		Work assignments model		Education model
	Medium	High	Medium	High	Medium	High	Medium	High
Variable	e^B	e^B	e^B	e^B	e^B	e^B	e^B	e^B
Female prison	1.941**		2.339***		1.841**		2.691***
Facility location
West^a	—	—	—		—	—	—	—
Northeast	6.201***	11.583***	1.314		0.819		1.338
South	2.537***		1.080		1.493	2.663***	0.685*
Midwest	1.884**		0.544*		0.704		1.095
Security level
Minimum^a	—	—	—	—	—	—	—	—
Medium	1.314		2.091***		0.854		1.934
Maximum	1.924**	0.911	3.476***	1.667**	1.122		1.628**
Facility size	1.001***		1.001***		1.001***		1.001***	1.001***
Constant	0.819	0.171***	1.617	0.661	1.110	0.226***	2.005*	0.897
LL	−986.316		−978.252		−875.817		−987.869
LR	178.08; p < .000		152.98; p < .000		109.48; p < .000		149.78; p < .000
McFadden’s pseudo R²	.107		.097		.083		.104
Brant	2.76; p = .737		14.92; p = .021		5.72; p = .455		10.06; p = .122
n (prisons)	1,015		1,037		942		1,037

Note. Coefficients for medium and high correspond to the logits formed from the contrasts {1, 23} and {12, 3}, respectively; e^B are exponentiated coefficients; constants are not exponentiated; blank cells exclude coefficients because they are redundant with the first column; — = interaction term excluded or not applicable; LL = log likelihood; LR = likelihood ratio test of full versus naïve model; Brant = omnibus test of proportional odds.

Reference group.

p < .05. **p < .01. ***p < .001.

Life-skills programming availability, unlike the other domains examined, met the POA and was therefore analyzed using OLR. For life-skills programming, gender significantly affected the odds that these types of programs were available in prisons with facilities housing females rather than males, having an increased odds of 332% of offering higher levels of life-skills programming (Table 6). Several of the facility-level characteristics also significantly influenced the level of life-skills programming available in prisons. Thus, for all five program domains, female facilities had significantly increased odds of offering higher levels of programming compared with male facilities.

Table 6.

Ordered Logistic Regression for Life-Skills Program Availability Levels (Census).

Variable	e^B
Female prison	4.317***
Facility location
West^a	—
Northeast	2.352***
South	0.581**
Midwest	1.248
Security level
Minimum^a	—
Medium	1.549**
Maximum	0.642*
Facility size	1.001***
LL	−1,020.34
LR	193.94; p < .000
McFadden’s pseudo R²	.087
Brant	10.05; p = .186
n (prisons)	1,037

Note. Response categories for life skills were 1 = low, 2 = medium, 3 = high; e^B are exponentiated coefficients; — = reference category or interaction term excluded; LL = log likelihood; LR = likelihood ratio test of full versus naïve model; Brant = omnibus test of proportional odds.

Reference group.

p < .05. **p < .01. ***p < .001.

For the second research question on program participation, we used binary logistic regression because of the use of dichotomous dependent variables (Hoffmann, 2004) to examine the relationship between inmate gender and involvement in certain programming options. Our analysis focused on several program domains such as mental health, substance abuse, education, vocational training, life skills, and three more specialized or targeted program areas (i.e., psychotropic medication, paid work, and parenting). Inmate gender was the independent variable of interest for each of these models. In addition, the models controlled for several individual- and facility-level variables including recognized needs for each domain type (e.g., mental health history, abuse history for participation in any type of mental health services or programming). In addition, to account for any errors due to the clustered nature of the data (i.e., inmates within facilities), clustered-robust standard errors were included with each model (Cameron & Miller, 2010).

Table 7 presents the results of the binary logistic models for inmate participation in the mental health and substance abuse program domains and for receiving psychotropic medication while in prison. The overall models for each were significant. Female inmates had increased odds of 60% compared with male inmates of participating in any type of mental health program or treatment and increased odds of 58% of receiving psychotropic medication during their imprisonment. In addition, Black inmates and inmates identified as “Other” (i.e., inmates who classified themselves as a race other than White or Black) had significantly decreased odds of participating in these programs compared with their White counterparts. As expected, histories of mental health problems or experiencing physical or sexual abuse (or what we call “recognized needs”) significantly increased the odds of participating in mental health programming and receiving psychotropic medications. In particular, having a prior mental health diagnosis resulted in a 2,347% increased likelihood of participating in some form of mental health programming and a 3,492% increased likelihood of receiving psychotropic medication during their prison term. Finally, other control variables including inmate age, having at least one rule violation, and the type of current offense were also significant for inmate involvement in mental health programming and in receiving psychotropic medication (see Table 7).

Table 7.

Binary Logistic Regression Models for Participation in Mental Health and Substance Abuse Program Areas (Survey).

	Mental health		Psychotropic medication		Substance abuse
Variable	B (SE)	e^B	B (SE)	e^B	B (SE)	e^B
Females	0.47 (0.12)	1.60***	0.46 (0.13)	1.58***	0.09 (0.09)	1.10
Race
White^a	—	—	—	—	—	—
Black	−0.15 (0.08)	0.86*	−0.19 (0.08)	0.82*	0.01 (0.06)	1.01
Other	−0.21 (0.10)	0.81*	−0.31 (0.10)	0.73**	−0.01 (0.08)	0.99
Mental health history	3.20 (0.08)	24.47***	3.58 (0.10)	35.92***
Physical abuse	0.48 (0.08)	1.62***	0.29 (0.08)	1.33***
Sexual abuse	0.44 (0.10)	1.55***	0.26 (0.10)	1.29**
Mandatory drug treatment					0.78 (0.07)	2.18***
Alcohol dependency					0.06 (0.01)	1.06***
Drug dependency					0.07 (0.01)	1.07***
Age	0.01 (0.00)	1.01***	0.02 (0.00)	1.02***	−0.00 (0.00)	1.00
Criminal history	−0.00 (0.01)	1.00	0.00 (0.01)	1.00	0.02 (0.01)	1.02
Time served	0.00 (0.00)	1.00**	−0.00 (0.00)	1.00	0.01 (0.01)	1.01***
Rules violation	0.44 (0.07)	1.56***	0.43 (0.08)	1.54***	0.21 (0.06)	1.23***
Current offense
Violent	0.30 (0.11)	1.35**	0.38 (0.11)	1.46**	−0.13 (0.09)	0.87
Property	0.15 (0.11)	1.16	0.30 (0.11)	1.35**	−0.01 (0.10)	0.99
Drug	−0.17 (0.12)	0.85	0.05 (0.12)	1.05	0.10 (0.10)	1.11
(continued)
Other^a	—	—	—	—	—	—
Facility location
West^a	—	—	—	—	—	—
Northeast	0.31 (0.15)	1.36*	0.18 (0.16)	1.19	0.26 (0.15)	1.29
South	−0.14 (0.13)	0.87	−0.10 (0.14)	0.91	−0.23 (0.14)	0.79
Midwest	−0.09 (0.14)	0.92	−0.08 (0.16)	0.92	−0.22 (0.15)	0.80
Facility security level
Minimum^a	—	—	—	—	—	—
Medium	0.05 (0.15)	1.06	0.04 (0.18)	1.04	−0.02 (0.13)	0.99
Maximum	0.31 (0.16)	1.36	0.40 (0.20)	1.49*	−0.30 (0.15)	0.74*
Facility size	0.00 (0.00)	1.00	0.00 (0.00)	1.00	−0.00 (0.00)	1.00***
LL	−3,773.041		−3,155.577		−6,172.633
LR	2,401.28***		1,955.74***		603.48***
McFadden’s pseudo R²	.375		.398		.087
n (inmates)	13,084		13,107		10,439

Note. LL = log likelihood; LR = likelihood ratio test of full versus naїve model.

Reference group.

p < .05. **p < .01. ***p < .001.

For substance abuse treatment, inmate gender did not significantly influence program participation (i.e., male and female prisoners participated at similar rates). As shown in Table 7, having a “recognized need” as was defined by the presence of a mandatory drug treatment order, self-reported drug dependency, and alcohol dependency significantly influenced the participation of inmates in some form of substance abuse programming. For instance, having a mandatory drug treatment order increased the odds of participating in a program by 118%. Several of the control variables (i.e., rules violations, time served, facility size) also significantly influenced the likelihood of participation in substance abuse programming.

Table 8 displays the binary logistic results for inmate participation in educational programming, vocational training, and life-skills programming. The models for each are significant and female prisoners were significantly more likely than male prisoners to participate in all three program areas. The results indicate that the odds of participating in this type of programming increased by 46% for female inmates compared with male inmates. In addition, while many of the control variables (i.e., age, time served, rules violations, current offense, security level, and size of the facility) were significant, mixed results were found for key background measures (i.e., “recognized needs”). Educational level significantly influenced participation for inmates, specifically decreasing the odds by 31% for those with a high school diploma and 54% for those with at least some college, while another “recognized need” (employment history) did not directly affect participation. This finding would seem to indicate that educational programming is being targeted more so to those prisoners who have the least amount of education.

Table 8.

Binary Logistic Regression Models for Participation in Education and Life-Skills Program Areas (Survey).

	Education		Vocational education		Life skills
Variable	B (SE)	e^B	B (SE)	e^B	B (SE)	e^B
Females	0.38 (0.10)	1.46***	0.32 (0.10)	1.38**	0.64 (0.13)	1.89***
Race
White^a	—	—			—	—
Black	0.08 (0.05)	1.08	0.12 (0.06)	1.13*	0.08 (0.06)	1.08
Other	0.20 (0.07)	1.22**	0.09 (0.07)	1.10	0.13 (0.07)	1.14
Employment history	0.03 (0.05)	1.03	0.14 (0.05)	1.15**	0.20 (0.05)	1.22***
Education
<High school^a	—	—	—	—
Diploma/GED	−0.38 (0.07)	0.69***	0.25 (0.07)	1.28**
Some college or more	−0.77 (0.10)	0.46***	0.48 (0.10)	1.62***
Children			0.11 (0.05)	1.12*	0.27 (0.04)	1.32***
Age	−0.03 (0.00)	0.97***	−0.02 (0.00)	0.98***	−0.02 (0.00)	0.98***
Criminal history	−0.02 (0.01)	0.98	−0.00 (0.01)	1.00	0.01 (0.01)	1.01
Time served	0.01 (0.00)	1.01***	0.01 (0.00)	1.01***	0.01 (0.00)	1.01***
Rules violation	0.50 (0.05)	1.66***	0.62 (0.06)	1.85***	0.38 (0.05)	1.46***
Current offense
Violent	0.33 (0.09)	1.40***	0.32 (0.10)	1.37**	0.20 (0.09)	1.22*
Property	0.10 (0.09)	1.10	0.23 (0.10)	1.26*	0.18 (0.09)	1.19
Drug	0.15 (0.09)	1.17	0.19 (0.09)	1.21*	0.24 (0.10)	1.27*
Other^a	—	—	—	—	—	—
(continued)
Facility location
West^a	—	—	—	—	—	—
Northeast	0.16 (0.13)	1.17	0.37 (0.16)	1.45*	0.16 (0.16)	1.17
South	−0.14 (0.11)	0.87	−0.08 (0.12)	0.92	−0.64 (0.13)	0.53***
Midwest	−0.09 (0.11)	1.10	−0.23 (0.15)	0.80	−0.59 (0.14)	0.56***
Facility security level
Minimum^a	—	—	—	—	—	—
Medium	0.03 (0.10)	1.04	0.32 (0.13)	1.38*	0.03 (0.13)	1.03
Maximum	−0.30 (0.11)	0.74**	−0.05 (0.15)	0.95	−0.45 (0.14)	0.64**
Facility size	−0.00 (0.00)	1.00***	0.00 (0.00)	1.00	−0.00 (0.00)	1.00***
LL	−7,160.301		−6,564.653		−7,013.964
LR	619.07***		671.05***		444.38***
McFadden’s pseudo R²	.076		.099		.073
N (inmates)	12,985		12,951		12,965

Note. LL = log likelihood; LR = likelihood ratio test of full versus naїve model.

Reference category.

p < .05. **p < .01. ***p < .001.

For vocational training, women inmates had increased odds of 38% for receiving some type of vocational skills during their time in prison. Being employed prior to prison and having received more education were positively related to participating in this type of training. In addition, other control measures such as younger inmates, those having served more time in prison, and inmates who had a history of violating prison rules and committing violent, property, or drug offense as opposed to some other offense, all resulted in an increased odds of participating in vocational training programs (Table 8). For participation in any type of life-skills programming, gender was once again significant. Female inmates had 89% increased odds of participating compared with their male counterparts. Background experiences or what we refer to as “recognized needs,” including having a history of employment (i.e., having a job in the 6 months prior to incarceration) and having minor children both significantly increased the odds of inmates participating in these programs (22% and 31%, respectively). Several control variables (i.e., age, time served, rules violations, current offense, facility location, security level, and size) were significantly related to participation in some type of life-skills programming (Table 8).

The final part of the analysis for program participation is presented in Table 9, and examines the relationship between inmate gender and involvement in prison work assignments, being paid for work, and participation in parenting programs. Although the overall binary logistic regression models are significant, inmate gender significantly influenced participation for only parenting programs. It would appear that male and female inmates are assigned to work positions and paid for work at similar rates. Several control variables were also influential in terms of work assignments and paid work, notably inmate age, time served, facility security level, and size. Inmate gender was significantly related to involvement in parenting programs. As expected, female inmates had 231% increased odds of participating in some form of parenting programming compared with male inmates. In addition, having minor children (i.e., “recognized need”) also understandably increased the odds of participation by 239% and several control variables (i.e., age, time served, rules violations, facility size) were also influential for participation in this type of programming (Table 9).

Table 9.

Binary Logistic Regression Models for Participation in Work and Parenting Program Areas (Survey).

	Work assignments		Paid work		Parenting
Variable	B (SE)	e^B	B (SE)	e^B	B (SE)	e^B
Females	0.19 (0.13)	1.21	0.12 (0.19)	1.13	1.20 (0.15)	3.31***
Race
White^a	—	—	—	—	—	—
Black	0.02 (0.06)	1.02	0.03 (0.08)	1.03	0.04 (0.09)	1.04
Other	−0.14 (0.08)	0.87	−0.09 (0.09)	0.91	0.10 (0.11)	1.11
Employment history					0.14 (0.08)	1.15
Children					1.22 (0.10)	3.39***
Age	0.02 (0.00)	1.02***	0.01 (0.00)	1.01***	−0.03 (0.00)	0.97***
Criminal history	−0.03 (0.01)	0.97**	−0.00 (0.01)	1.00	−0.01 (0.01)	0.99
Time served	0.00 (0.00)	1.00**	0.00 (0.00)	1.00***	0.01 (0.00)	1.01***
Rules violation	0.04 (0.06)	1.05	0.01 (0.08)	1.01	0.32 (0.09)	1.37***
Current offense
Violent	0.13 (0.08)	1.14	0.20 (0.10)	1.23*	0.03 (0.14)	1.03
Property	0.06 (0.08)	1.06	0.10 (0.10)	1.11	−0.02 (0.15)	0.98
Drug	0.12 (0.07)	1.12	0.06 (0.09)	1.06	0.15 (0.15)	1.17
Other^a	—	—	—	—	—	—
(continued)
Facility location
West^a	—	—	—	—	—	—
Northeast	0.19 (0.17)	1.21	0.44 (0.27)	1.55	−0.20 (0.23)	0.82
South	0.44 (0.14)	1.56**	−1.43 (0.27)	0.24**	−0.98 (0.22)	0.38***
Midwest	0.00 (0.17)	1.00	0.11 (0.24)	1.11	−0.45 (0.23)	0.64
Facility security level
Minimum^a	—	—	—	—	—	—
Medium	−0.20 (0.17)	0.82	−0.10 (0.28)	0.91	0.08 (0.19)	1.08
Maximum	−0.59 (0.19)	0.56**	−0.77 (0.29)	0.46**	−0.31 (0.18)	0.73
Facility size	−0.00 (0.00)	1.00***	−0.00 (0.00)	1.00***	−0.00 (0.00)	1.00**
LL	−8,004.62		−7,594.33		−3,370.37
LR	171.14***		212.07***		480.02***
McFadden’s pseudo R²	.049		.1,354		.096
n (inmates)	13,301		13,281		12,969

Note. LL = log likelihood; LR = likelihood ratio test of full versus naїve model.

Reference category.

p < .05. **p < .01. ***p < .001.

In sum, gender significantly affected many of the program areas examined, with female inmates often participating more than male inmates. Notably, though, gender did not significantly affect participation in substance abuse treatment or work assignments. Still, women were more likely to participate in many services and programs more frequently than men. In addition, many individual- and facility-level factors also influenced programming participation, as did a series of anticipated “recognized needs.”

Discussion

In the current study, we examined the availability of and participation in programs for male and female inmates in state prisons across the United States. Specifically, we explored the relationship between gender and a variety of program areas including some that are seen as addressing specific areas of need for female prisoners (i.e., mental health care and life-skills programming) and some that have been criticized for being stereotypical for them (i.e., use of psychotropic medications and parenting programs). Using Census data (2000) of state prisons, we compared the levels of programming that were offered in male and female prisons. For program participation by inmates, we used survey data (2004) to consider whether or not inmate gender influenced receiving certain services or participating in general areas of programming. In addition, we focused our analysis on the relationship between inmate gender and receiving psychotropic medications, being paid for work at the prison, and involvement in parenting programs. Our results using these two national data sets are mixed when compared with findings from an earlier national study in the United States, indicating some changes have occurred in prisons (Morash et al., 1994).

First and foremost, we found that gender (inmate and institution) matters in correctional programming. Gender is significant; it influenced most of the programming options examined, including both availability and participation. There was a wider variety of programs offered in female prisons compared with male prisons for medical care, mental health, work assignments, education, and life skills. These results, when compared with previous research, are mixed. Researchers have frequently noted a lack of programming for women in prisons, especially vocational and education programs (Lee, 2000; Rafter, 1990; Zatz, 2000). However, Morash and colleagues (1994) noted more programming was available in these areas for women than for men. Considering these higher levels (i.e., greater variety) of programming in female institutions, state corrections systems seem to recognize that their female offender population has a higher and, also possibly, a different set of needs than their male counterparts. For instance, we know from research that a higher proportion of women compared with men prisoners are identified as having a mental illness (Morash & Schram, 2002), and that women are more inclined to self-report a history of abuse and trauma (Anderson, 2003). Therefore, prisons may be responding to the higher level of needs of female inmates with more frequent and varied programming. It may also be that the push for equity in female prisons regarding services, whether affected directly by litigation or not, likely influenced the programs offered to women, especially in the areas of medical and mental health, and vocational and education programs (Kruttschnitt & Gartner, 2003; Morash & Schram, 2002). However, simply knowing whether or not a program is available to inmates does not tell us about the size of the program, or whether the program is meeting the demand for services.

Regarding participation, women were more likely to report receiving some type of mental health care in general and mental health psychotropic medications more specifically. Almost one third of women reported using psychotropic medications (33%) and had increased odds of 59% of receiving medications compared with male inmates, even after accounting for explanatory factors (i.e., mental health history, abuse history). Notably, only 16% of women in the mid-1980s were prescribed psychotropic medications (Morash et al., 1994), whereas 33% of women in this study were, meaning that the number of women being medicated in prisons has essentially doubled between 1986 and 2004. This trend has been noted by other studies with James and Glaze (2006) reporting that the proportion of U.S. inmates in state prisons, both male and female, receiving any type of mental health treatment has risen since the late 1990s; women, however, were told they had a mental disorder at three times the rate of men.

It would, therefore, appear that incarcerated women are increasingly being “managed” in state prisons with medications, a trend that has also been noted in general society where “aggressive women” are managed through prescription medications (Baskin, Sommers, Tessler, & Steadman, 1989). There are considerable concerns that U.S. prisons are resorting to the use of psychotropic medication as a strategy for treating mentally ill incarcerated women (Kilty, 2012). However, research has also indicated that women in general society are much more likely to use drugs for mental health issues than men, with around a quarter of women in the United States taking some sort of medication (Medco, Inc., 2010). This is an alarming trend that highlights the need to determine whether the increasing use of medications is actually needed for treatment purposes (Anderson, 2003), or whether their use is a form of control and indicative of our stereotypical views of women as being irrational and crazy (Ussher, 2010), especially considering the calls for an increased attention to women’s mental health in general, which is described as underfunded and under-researched (Kulkarni, 2014).

Work assignments and education programs are both viewed as being important to provide to inmate populations because men and women often arrive into custody with strikingly low levels of occupational achievement and educational attainment (Belknap, 2010; Bloom et al., 2005; Mumola, 2000; Owen, 1998). In our study, we found that prisons for women were significantly more likely to make available prison industries and work release assignments than prisons for men. It could be that prisons are more apt to allow work release assignments for women because it may be assumed that they pose less of a flight risk or security concern. Incarcerated women in our sample were also more likely to participate in education and vocational programs and job training compared with male inmates. Where Morash and colleagues (1994) found that women were not as likely as men (63% vs. 72%) to be paid for work they did while incarcerated, we found in the current study that incarcerated men and women were being paid for their work at similar, but comparatively low rates (38% vs. 39%). It might be that outside influences of more equal pay for women in the general U.S. population (Bureau of Labor Statistics, 2010) are mirrored in prisons, with the equity of pay for work also rising over the last two decades. Overall, women are slightly more likely than men to take advantage of educational and vocational programming. The finding that women are both being offered and participating in these types of programs is especially positive because women need such programs to better prepare themselves for community re-entry.

Life-skills programming and parenting programs were examined by gender with women reporting higher levels of availability and being more likely to participate than men for both. Our findings are consistent with prior research focusing on parenting programs within the corrections system (Glaze & Maruschak, 2010; Morash et al., 1994). Surprisingly, the overall levels of participation for these programming options (8% of men and 20% of women) were not very high even though both incarcerated men and women are frequently parents (Glaze & Maruschak, 2010). Women may be more likely than men to participate in life-skills and parenting programs because they do not feel particularly good about their own parenting experiences, and because they are motivated to become better mothers and have plans to reunite with their children and families once they are released from prison (Arditti & Few, 2006; Morash & Schram, 2002).

Our results concerning parenting programs in prison presents criminologists and practitioners with an intriguing dilemma. Studies on incarcerated women consistently indicate that a high proportion of them are mothers and that they require programming that teaches skills and information about their children and roles as parents. Many would acknowledge that prisons are being responsive to their unique needs because women tend to be the primary caregivers prior to their incarceration (Belknap, 2007; Bloom et al., 2005; Glaze & Maruschak, 2010; Koons-Witt, 2002), thus parenting programs may indeed be indicative of gender-responsive programming. Yet, given that women were much more likely to be offered to participate in these programs, the findings highlight the significance of Morash’s (2010) warning that gender-responsive programs might work to reinforce stereotypical gender roles and expectations, especially if parenting programs are being offered at the expense of other program options. It could be that these programs, rather than being offered based on gender-responsive ideals, are being offered because they work to fulfill some idealized form of femininity (i.e., women are responsible for children and should be good mothers). Furthermore, scholars have consistently noted women’s institutions continuance of utilizing programs that teach women domestic and child-rearing classes (Bosworth, 2003; Franklin, 2008; Lee, 2000; Morash et al., 1994) despite the fact that women are more than just mothers, and besides needing parenting programs, they also need programs that address mental health and substance abuse needs, education, and provide employable skills (Belknap, 2010; Bloom et al., 2005; Greenfeld & Snell, 1999; Mumola, 2000). Although it is arguable, that many women are in need of these programs, when institutions focus on these issues over others or while ignoring others, they may be doing a disservice to women.

We also know from state and national data that incarcerated men tend to be fathers to children who remain behind in the community (Glaze & Maruschak, 2010), however only 40% of male institutions offered this programming even though many men could benefit from these parenting programs. We believe that male prisoners would also greatly benefit from parenting classes and programs that promote closer and healthier relationships with their kids. However, there might be justifiable reasons why male prisons do not offer parenting classes at the rate of women’s prison. One reason may be the attitudes of the male prisoners, themselves. It may be that there is little interest in parenting programs on the part of male inmates, and therefore, prisons do not feel the need to offer these programs. This disinterest or low motivation may come from the fact that males in prison are less likely than women in prison to be the primary caregiver of their children (Belknap, 2007), even though it might be assumed that stereotypical notions of being a “good father” should have some influence over programming, especially considering it seems to have an effect on programming for women. Nevertheless, it may be a more complex issue for male offenders because they may have children with multiple women, and their contact and motivations to become better fathers may be influenced by maternal gatekeeping, especially by the focal mother (Arditti, Smock, & Parkman, 2005; Roy & Dyson, 2005). Finally, often when men are incarcerated, the mother of the child(ren) assumes more parenting responsibilities, which is not often the case for women who are incarcerated (Mumola, 2000). The fact that incarcerated men are significantly less likely than incarcerated women to have access to parenting programs and to participate in them suggests that prisons may be reinforcing traditional gender stereotypes for both men and women inmates, with the emphasis being on women as mothers.

Finally, the findings concerning participation in substance abuse treatment and programming provided a different picture from many of our other results. Women and men were not significantly different in their participation in substance abuse treatment. Overall, the participation for these treatments was very low (37%), even though participation in these programs has seemingly increased since the 1980s when only around 14% of inmates (14% of females and 15% of males) reported receiving drug treatments (Morash et al., 1994). It seems that, compared with previous studies (e.g., Morash et al., 1994), women’s participation in substance abuse treatments is increasing. This finding also seems to mirror findings of research for women in the general population, which indicates that much like women in prison, historically, programs aimed at helping women overcome substance abuse were ill-equipped to handle women because they had been developed for men (Green, 2006). However, over the last several decades, programming in general has incorporated gender-specific needs and has become more successful in treating women (Green, 2006). In addition, women’s participation in such programming has risen and women are more currently seeking help for substance abuse problems at similar rates to their male counterparts (Green, 2006).

Our finding on substance abuse treatment, like several others, may indicate positive changes in available programming and treatment space. We know that men and women offenders frequently have extensive drug involvement and a family history of substance abuse and criminality (Giordano, Cernkovich, & Rudolph, 2002; National Research Council, 2008). However, our findings may indicate that programming has not yet increased enough to meet the needs of all inmates considering the extant research indicating the severe drug use among them. This may be particularly true for female inmates who have had more involvement with drugs and are more likely to come into prison due to drug crimes (Chesney-Lind, 2004; Greenfeld & Snell, 1999; Simon & Ahn-Redding, 2005).

In sum, many of the programs examined for availability and participation were significantly related to gender, and when there were significant differences, female facilities were more likely to offer higher level of programs in particular areas and women were more likely to participate in these programs. For some programs, though, participation rates were low for both men and women, a finding that has been noted in other studies (Morash et al., 1994). In addition, insignificant statistical findings are worth noting. This study, unlike previous studies, found that women and men were participating in substance abuse programming at similar rates and were being paid for work similarly. This seems to indicate a step forward from previous national-level findings where women were less likely to receive substance abuse treatment (Morash et al., 1994). Furthermore, gender stereotypes do not appear to be as influential as they once were in programming and prisons are now making programs that were once traditionally given to one gender available to both, although more work is needed in this area, especially considering that we were unable to determine the goals and/or efficacy of these programs. It seems that prisons may be improving in terms of the types of programs available to incarcerated women, however much more research is needed to determine whether programming is meeting the specific needs of women prisoners nationally and whether programming is effective for them.

The findings from our study are promising, but several questions remain. Our study is able to provide researchers and practitioners with an indication of what types of programs are being offered to men and women in state prisons, and whether or not they are participating in them, however the existing data cannot address whether a substance abuse program offered in a male prison uses the same (or different) treatment model as a substance abuse program offered in a female prison. In other words, we cannot determine whether women are receiving programs that are designed based on gender-responsive principles or whether they are receiving the “male model.” In addition, we could not determine how these programs were implemented or whether they were tailored to the needs of inmates or whether generic programming options were placed in facilities. Moreover, even though prisons might have programming available or “on the books,” that does not necessarily mean that all the programs available are being utilized or that program capacity is large enough.

Availability of programming in U.S. prisons and participation by inmates in programming was examined using two separate, but related analyses. Although it would have been important to control for program availability when looking at gender differences in program participation for state inmates, this was not possible given the constraints (i.e., different years, possible differences in the types of programs for which data are being collected) of the two data sets used. Instead, we are left to understand how program availability might have influenced inmate participation in programming by considering the findings from both data sources (availability and participation) side-by-side.

In addition, these data rely on self-reports from inmates, and as noted by Belenko and Houser (2012), these reports may have errors due to under or over reporting. Also, inmates were only asked to report whether they had participated in a program since admission, which cannot capture the length or completion of the program (Belenko & Houser, 2012). To address these types of questions, national surveys of prisons and inmates should go beyond the mere presence of programs and collect more comprehensive information on substantive aspects of programs that are given to male and female prisoners.

Footnotes

Appendix

Table A3.

Descriptives for Program Participation Outcome Measures (Survey).

Program domain service or program	Yes (f)	No (f)
Medical care
Tuberculosis testing	95.1% (13,189)	4.9% (676)
HIV testing	84.7% (9,271)	15.3% (1,679)
Medical exam	84.4% (11,707)	15.6% (2,157)
Pelvic exam^a	85.4% (800)	14.6% (137)
Dental treatment	43.1% (5,975)	56.9% (7,898)
Mental health
Psychotropic medication	15.1% (2,090)	84.9% (11,751)
Hospitalization	3.1% (424)	96.9% (13,415)
Counseling	12.7% (1,760)	87.3% (12,073)
Other	1.9% (261)	98.1% (13,555)
Substance abuse
Detoxification	0.7% (90)	99.3% (12,893)
Inpatient treatment	7.5% (978)	92.5% (12,001)
Outpatient treatment	5.2% (672)	94.8% (12,305)
Self-help/peer counseling	25.2% (3,276)	74.8% (9,699)
Education/awareness	15.1% (1,956)	84.9% (11,016)
Maintenance	0.2% (24)	99.8% (12,957)
Other	1.4% (181)	98.6% (12,801)
Recreation
Psychical exercise	59.7% (8,261)	40.3% (5,573)
Television	68.1% (9,424)	31.9% (4,410)
Reading	74.6% (10,312)	25.4% (3,517)
Phone calls	83.8% (11,573)	16.2% (2,244)
Other recreation	40.4% (5,595)	59.6% (8,242)
Religious	55.4% (7,666)	44.6% (6,166)
Work assignment
On-grounds	60.1% (8,317)	39.9% (5,520)
Off-grounds	7.5% (1,033)	92.5% (12,806)
(continued)
Janitorial work	18.6% (2,577)	81.4% (11,259)
Grounds/road maintenance	8.0% (1,111)	92.0% (12,725)
Food preparation	12.0% (1,660)	88.0% (12,177)
Laundry	3.2% (445)	96.8% (13,392)
Medical services	0.6% (86)	99.4% (13,750)
Farming/forestry/ranching	2.2% (310)	97.8% (13,526)
Goods production	3.3% (452)	96.7% (13,384)
Other services	6.6% (915)	93.4% (12,922)
Maintenance/construction	5.0% (695)	95.0% (13,141)
Other work assignments	10.4% (1,439)	89.6% (12,396)
Paid for work	38.1% (5,262)	61.9% (8,553)
Vocational training	27.5% (3,799)	72.5% (10,024)
Education
Basic education	2.0% (282)	98.0% (13,546)
High school/GED preparation	19.3% (2,672)	80.7% (11,155)
College courses	7.3% (1,003)	92.7% (12,825)
English as second language	1.0% (144)	99.0% (13,684)
Other educational programs	5.4% (750)	94.6% (13,077)
Life skills
Employment counseling	8.9% (1,229)	91.1% (12,585)
Parenting/child-rearing classes	8.3% (1,150)	91.7% (12,664)
Life-skills/community adjustment	23.5% (3,250)	76.5% (10,561)
Pre-release programs	5.4% (740)	94.6% (13,074)

Note. N = 14,499 inmates.

Female only.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Anderson

T. L.

(2003). Issues in the availability of health care for women prisoners. In Sharp

S. F.

(Ed.), The incarcerated woman: Rehabilitative programming in women’s prisons (pp. 49-60). Upper Saddle River, NJ: Prentice Hall.

Andrews

Bonta

Hoge

(1990). Classification for effective rehabilitation: Rediscovering psychology. Criminal Justice and Behavior, 17, 19-52.

Arditti

J. A.

Few

A. L.

(2006). Mothers’ reentry into family life following incarceration. Criminal Justice Policy Review, 17, 103-123.

Arditti

J. A.

Smock

S. A.

Parkman

T. S.

(2005). “It’s been hard to be a father”: A qualitative exploration of incarcerated fatherhood. Fathering, 3, 267-288.

Baskin

D. R.

Sommers

Tessler

Steadman

H. J.

(1989). Role incongruence and gender variation in the provision of prison mental health services. Journal of Health and Social Behavior, 30, 305-314.

Belenko

Houser

K. A.

(2012). Gender differences in prison-based drug treatment participation. International Journal of Offender Therapy and Comparative Criminology, 56, 790-810.

Belknap

(2003). Responding to the needs of women prisoners. In Sharp

S. F.

(Ed.), The incarcerated woman: Rehabilitative programming in women’s prisons (pp. 93-106). Upper Saddle River, NJ: Prentice Hall.

Belknap

(2007). The invisible woman: Gender, crime and justice (3rd ed.). Belmont, CA: Wadsworth.

Belknap

(2010). “Offending women”: A double entendre. The Journal of Criminal Law and Criminology, 100, 1061-1098.

10.

Bloom

Owen

Covington

(2005). Gender responsive strategies for women offenders: A summary of research, practice, and guiding principles for women offenders. Washington, DC: National Institute of Corrections, U.S. Department of Justice.

11.

Bosworth

(2003). Prison programs in the United States: Preparing inmates for release. In Roberts

(Ed.), Critical issues in crime and justice (pp. 318-394). Thousand Oaks, CA: Sage.

12.

Cameron

C. A.

Miller

D. L.

(2010). Robust inference with clustered data. In Ullah

Giles

D. E.

(Eds.), Handbook of empirical economics and finance (pp. 1-25). Boca Raton, FL: Chapman & Hall/CRC.

13.

Chesney-Lind

(2004). The female offender: Girls, women, and crime (2nd ed.). Thousand Oaks, CA: Sage.

14.

Covington

S. S.

Bloom

B. E.

(2006). Gender responsive treatment and services in correctional settings. Women & Therapy, 29(3-4), 9-33.

15.

Cullen

F. T.

Gendreau

(1989). The effectiveness of correctional treatment: Reconsidering the “nothing works” debate. In Goodstein

MacKenzie

D. L.

(Eds.), The American prison: Issues in research and policy (pp. 23-34). New York, NY: Plenum Press.

16.

Cullen

F. T.

Jonson

C. L.

(2011). Rehabilitation and treatment programs. In Wilson

J. Q.

Petersilia

(Eds.), Crime and public policy (2nd ed., pp. 293-344). New York, NY: Oxford University Press.

17.

Cullen

F. T.

Smith

Lowenkamp

C. T.

Latessa

E. J.

(2009). Nothing works revisited: Deconstructing Farabee’s Rethinking Rehabilitation. Victims & Offenders, 4, 101-123.

18.

Daly

(1992). Women’s pathways to felony court: Feminist theories of lawbreaking and problems of representation. Southern California Review of Law and Women’s Studies, 2, 11-52.

19.

Davis

A. Y.

Shaylor

(2001). Race, gender, and the prison industrial complex: California and beyond. Meridians, 2, 1-25.

20.

Franklin

C. A.

(2008). Women offenders, disparate treatment, and criminal justice: A theoretical, historical, and contemporary overview. Criminal Justice Studies, 21, 341-360.

21.

French

S. A.

Gendreau

(2006). Reducing prison misconducts: What works! Criminal Justice and Behavior, 33, 185-218.

22.

(1998). Estimating generalized ordered logit models. Stata Technical Bulletin, 44, 27-30.

23.

Giordano

P. C.

Cernkovich

S. A.

Rudolph

J. L.

(2002). Gender, crime, and desistance: Toward a theory of cognitive transformation. The American Journal of Sociology, 107, 999-1064.

24.

Glaze

L. E.

(2010). Correctional populations in the United States, 2009. Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice.

25.

Glaze

L. E.

Maruschak

L. M.

(2010). Parents of prison and their minor children. Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice.

26.

Goodstein

MacKenzie

D. L.

(1989). The American prison: Issues in research and policy. New York, NY: Plenum Press.

27.

Green

C. A.

(2006). Gender and use of substance abuse treatment services. Alcohol Research & Health, 29(1), 55-62.

28.

Greenfeld

L. A.

Snell

T. L.

(1999). Special report: Women offenders. Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice.

29.

Henrichson

Delaney

(2012). The price of prisons: What incarceration costs taxpayers. New York, NY: Vera Institute of Justice.

30.

Hoffmann

J. P.

(2004). Generalized linear models: An applied approach. Boston, MA: Pearson.

31.

Huebner

B. M.

DeJong

Cobbina

(2010). Women coming home: Long-term patterns of recidivism. Justice Quarterly, 27, 225-254.

32.

Ignatieff

(1981). State, civil society, and total institutions: A critique of recent social histories of punishment. Crime and Justice, 3, 153-192.

33.

James

D. J.

Glaze

L. E.

(2006). Mental health problems of prison and jail inmates. Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice.

34.

Kilty

J. M.

(2012). “It’s like they don’t want you to get better”: Psy control of women in the carceral context. Feminism & Psychology, 22, 162-182.

35.

Kirchhoff

S. M.

(2010). Economic impacts of prison growth. Congressional Research Service. https://www.fas.org/sgp/crs/misc/R41177.pdf

36.

Koons-Witt

B. A.

(2002). The effect of gender on the decision to incarcerate before and after the introduction of sentencing guidelines. Criminology, 40, 297-327.

37.

Koons-Witt

B. A.

Burrow

J. D.

Morash

Bynum

(1997). Expert and offender perceptions of program elements linked to successful outcomes for incarcerated women. Crime & Delinquency, 43, 512-532.

38.

Kruttschnitt

Gartner

(2003). Women’s imprisonment. Crime and Justice, 30, 1-81.

39.

Kulkarni

(2014). Women’s mental health: Still not a priority, still not good enough. Australian & New Zealand Journal of Psychiatry, 48, 701-704.

40.

Lee

J. A.

(2000). Women prisoners, penological interests, and gender stereotyping: An application of equal protection norms to female inmates. Columbia Human Rights Law Review, 32, 251-288.

41.

Lillis

(1994). Prison education programs reduced. Corrections Compendium, 19(3), 1-11.

42.

MacKenzie

D. L.

(2000). Evidence-based corrections: Identifying what works. Crime & Delinquency, 46, 457-471.

43.

Medco, Inc. (2010). America’s state of mind: A report by Medco. Orange Park, FL: Author.

44.

Meskell

M. W.

(1999). An American resolution: The history of prisons in the United States from 1777 to 1877. Stanford Law Review, 51, 839-865.

45.

Morash

(2010). Women on probation and parole: A feminist critique of community programs and services. Lebanon, NH: Northeastern University Press.

46.

Morash

Haarr

R. N.

Rucker

(1994). A comparison of programming for women and men in U.S. prisons in the 1980s. Crime & Delinquency, 40, 197-221.

47.

Morash

Robinson

A. L.

(2002). Correctional administrators’ perspectives on gender arrangements and family-related programming for women offenders. Marriage & Family Review, 32, 83-109.

48.

Morash

Schram

P. J.

(2002). The prison experience: Special issues of women in prison. Prospect Heights, IL: Waveland Press.

49.

Mumola

C. J.

(2000). Incarcerated parents and their children (Bureau of Justice Statistics Special Reports). Washington, DC: U.S. Department of Justice.

50.

National Research Council. (2008). Parole, desistance from crime, and community integration. Washington, DC: National Academy of Sciences.

51.

Owen

(1998). “In the mix”: Struggle and survival in a women’s prison. Albany: State University of New York Press.

52.

Phelps

M. S.

(2011). Rehabilitation in the punitive era: The gap between rhetoric and reality in U.S. prison programs. Law & Society Review, 45, 33-68.

53.

Porter

N. D.

(2011). On the chopping block: State prison closings. Washington, DC: The Sentence Project, Research and Advocacy for Reform.

54.

Rafter

N. H.

(1990). Partial justice: Women, prisons, and social control. New Brunswick, NJ: Transaction.

55.

Roy

K. M.

Dyson

O. L.

(2005). Gatekeeping in context: Babymama drama and the involvement of incarcerated fathers. Fathering, 3, 289-310.

56.

Salisbury

E. J.

Van Voorhis

(2009). Gendered pathways: A quantitative investigation of women probationers’ path to incarceration. Criminal Justice and Behavior, 36, 541-566.

57.

Simon

R. J.

Ahn-Redding

(2005). The crimes women commit: The punishments they receive (3rd ed.). Lanham, MD: Lexington Books.

58.

U.S. Bureau of Labor Statistics. (2010). Highlights of women’s earnings in 2009: Report 1025. Author. Available from www.bls.gov

59.

U.S. Census Bureau. (2015). Geographic terms and concepts: Census divisions and Census Regions. www.census.gov

60.

U.S. Department of Justice, Bureau of Justice Statistics. (2000). CENSUS OF STATE AND FEDERAL ADULT CORRECTIONAL FACILITIES [Computer file]. Conducted by U.S. Department of Commerce, Bureau of the Census (ICPSR ed.). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [producer and distributor], 2004. doi:10.3886/ICPSR04021

61.

U.S. Department of Justice, Bureau of Justice Statistics. (2004). SURVEY OF INMATES IN STATE AND FEDERAL CORRECTIONAL FACILITIES [Computer file] (ICPSR04572-v1). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [producer and distributor], 2007-02-28. doi:10.3886/ICPSR04572

62.

Ussher

J. M.

(2010). Are we medicalizing women’s misery? A critical review of women’s higher rates of reported depression. Feminism & Psychology, 20(9), 9-35.

63.

Williams

(2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The State Journal, 6, 58-82.

64.

Zatz

(2000). The convergence of race, ethnicity, gender, and class on court decisionmaking: Looking toward the 21st century. In Horney

(Ed.), Policies, processes, and decisions of the criminal justice system: Criminal justice 2000 (Vol. 3, pp.502-552). Washington, DC: Department of Justice.