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. Author manuscript; available in PMC: 2025 Aug 1.
Published in final edited form as: Stigma Health. 2023 Feb 9;9(3):303–310. doi: 10.1037/sah0000427

Internalized Shame Among Justice-Involved Women in Substance Use Disorder Treatment: Measurement Invariance and Changes During Treatment

Verlin W Joseph 1, David IK Moniz-Lewis 1, Dylan K Richards 1, Matthew R Pearson 1, Jason B Luoma 2, Katie Witkiewitz 1
PMCID: PMC11293465  NIHMSID: NIHMS1874140  PMID: 39099891

Abstract

Shame is one of the leading barriers to successful recovery in substance use treatment settings. This secondary analysis study examined measurement invariance of the Internalized Shame Scale (ISS) and explored changes in shame during treatment. Participants (N=105) in the parent study were recruited from a nonprofit residential treatment center for justice-involved women and were randomized to receive mindfulness-based relapse prevention or relapse prevention treatment. A series of confirmatory factor analyses were used to assess measurement invariance in a one-factor measurement model of the ISS. Latent growth curve modeling was used to examine change in shame over time. Our findings support the assumption of measurement invariance across multiple time points and across treatment conditions, supporting comparisons of stigma scores across groups and over time. Although we observed significant reductions in shame from pre- to post-treatment, there were no differences across treatment conditions. Additional research is needed to determine how distinct treatment components relate to reductions in shame among individuals receiving treatment for a substance use disorder.

Keywords: substance use disorder, internalized stigma, shame, mindfulness-based relapse prevention, relapse prevention, criminal justice

Introduction

An estimated 19.7 million people met the criteria for a substance use disorder (SUD); and roughly 20% of those who met criteria for SUD received SUD treatment in the United States in 2017 (SAMHSA, 2018). One of the reasons for the lack of treatment seeking among individuals with SUD is the stigma associated with SUD and SUD treatment. Stigma, defined as attitudes, beliefs, behaviors, and structures that interact at different levels of society (i.e., individuals, groups, organizations, systems) and manifest in prejudicial attitudes and discriminatory practices against people with mental health disorders and/or SUD, poses a threat at each step of the addiction recovery continuum (Social et al., 2016). The World Health Organization found SUD to be the 1st or 2nd most stigmatized health condition across 12 of 14 countries examined (Kulesza et al., 2014).

Prior to engaging in treatment, stigma and the fear of stigmatization can decrease the likelihood of an individual with a SUD from seeking treatment (Hammarlund et al., 2018; Stringer & Baker, 2018). Additionally, stigma has been found to increase apathy towards accomplishing one’s goals and decrease the likelihood of one moving towards one’s goals – such as seeking out treatment (Corrigan et al., 2016). For those seeking treatment for SUDs, stigma may affect each stage of recovery during treatment and after treatment (Howard, 2015; Link et al., 1997; van Olphen et al., 2009; Witkiewitz, Warner, et al., 2014). Stigma not only impacts the quality of care that individuals with SUDs receive while in healthcare settings, but it is associated with changes in the duration of stay in treatment settings, decreased self-esteem while in treatment, increased psychological distress, impaired quality of life, and decreased social functioning (Biancarelli et al., 2019; Cheng et al., 2019; Luoma et al., 2007; Rodrigues et al., 2013).

Perhaps most consequential effect of stigma on individuals is internalized shame, which results when an individual internalizes stigmatizing beliefs and devalues themselves as a result (Luoma, Kohlenberg, Hayes, & Fletcher, 2012). Empirically, shame is associated with substance use problems at a level similar to depression (Luoma, Chwyl, & Kaplan, 2019), suggesting that reducing shame in people with a SUD may serve as a critical step towards the improvement of substance use outcomes, a person’s negative view of themselves and their ability to recover from SUD following treatment. One study by Randles and Tract (2013) found that non-verbal indicators of shame Alcoholics Anonymous participants who were abstinent strongly predicted the likelihood of returning to substance use in the coming year, the severity of the return to substance use, and detrimental health declines (Randles & Tracy, 2013).

Criminal legal system stigma has also been linked to various detrimental direct and indirect health outcomes (Martin et al., 2020). The stigma associated with the criminal legal system has been identified as being a barrier to substance use treatment in general (Hartwell, 2014), which is likely amplified among legal system-involved women. For example, women with children are more likely than men to be stigmatized for losing custody of their children due to problems with substance use (Stone, 2015). Criminal legal system related stigma is positively correlated with recidivism, severity of SUD, mental health symptoms, and poor community adjustment among women (Moore et al., 2016).

Given these outcomes, it may be important to directly address stigma and internalized shame among individuals in the criminal legal system and one potential approach for addressing stigma, shame, and related outcomes among individuals is mindfulness-based interventions (Brem et al., 2017; Goodarzi et al., 2020; Luoma et al., 2014; Malouf et al., 2017; Woods & Proeve, 2014). Mindfulness refers to the intentional regulation of one’s attention to what is arising in the present moment (such as thoughts, emotions, bodily sensations) with an attitude of non-judgment and acceptance (Kabat-Zinn, 1994). Mindfulness-based interventions have been shown to have benefits on an array of psychological variables in both clinical and non-clinical populations, including the reduction of shame (Eberth & Sedlmeier, 2012; Li, 2016; Piet & Hougaard, 2011). In a study by Sedighimornani and colleagues (2019), trait mindfulness was found to be negatively correlated with shame, with the specific mindfulness facet of non-judgment (i.e., accepting one’s thoughts and emotions without critique or criticism) being most strongly negatively correlated with shame.

Goodarzi and colleagues (2020) reported correlations between reductions in symptoms of shame, anxiety, and depression among female sexual assault victims after receiving a mindfulness-based treatment (Goodarzi et al., 2020). Malouf and colleagues (2017) reported reductions in criminal recidivism among participants receiving the mindfulness-based intervention as compared to a treatment as usual control group. However, the aforementioned studies did not assess the effects of shame on substance use outcomes. An open trial (Luoma et al., 2007) and randomized pilot trial (Luoma et al., 2012) found acceptance and commitment therapy, which includes mindfulness-based components, was successful in reducing internalized shame in a sample of people with SUD in residential treatment, many of whom were also legal system-involved.

As noted above, stigma may result in internalized shame, and although stigma and shame related the constructs, they are not entirely the same. Stigma generally refers to the application of negative stereotypes and behaviors towards an individual, whereas shame applies to the emotional experience of being stigmatized (Luoma & Platt, 2015). Corrigan’s progressive model of self-stigma details how stigmatizing attitudes may result in shame among marginalized individuals. According to the model, there are three distinct succeeding stages of stigma including: being aware of associated stereotypes, agreeing with them, applying the stereotype to oneself, and suffering lower self-esteem (Corrigan et al., 2011). The model assumes stigma has an internalized “trickle down” effect which facilitates stigmatizing beliefs and shame among individuals. The model also assumes a linear progression of stigma through the aforementioned stages, with stronger associations occurring during the earlier domains compared to the latter stages (Corrigan et al., 2011; Göpfert et al., 2019). Although, “internalized shame” and “internalized stigma” are often used interchangeably, the latter is an ongoing process resulting in shame. For the remainder of this paper, we will be referring to internalized shame as measured by the Internalized Shame Scale (ISS).

The Internalized Shame Scale (ISS) is one of the few measures assessing internalized shame as is based on the idea that the internalization arises from abuse and shame and losses in ones family, and that addictive behaviors are in part a type of defensive behavior intended to reduce the pain of shame, which ironically can contribute to more shame (Cook, 1998). The scale was originally developed for use with people with addictive behaviors and the psychometrics have been studied in substance using, general adult, university student, and clinical sample. A recent measurement review paper found the ISS to have good construct validity, with some evidence for content validity, test-retest reliability, and internal consistency (Lear, Lee, Smith, & Luoma, 2022) but measurement invariance has yet to be studied.

In order to examine the relationship between SUD treatment and internalized shame, one must first establish that the measure of internalized shame is invariant across study groups and over time. Measurement invariance refers to the degree to which the same theoretical construct is captured under different conditions (Putnick, Diane & Bornstein, Mark, 2016). Levels of measurement invariance include configural invariance, metric invariance, and scalar invariance. Configural invariance is achieved if all groups or measurements have the same factor structure (i.e., all of the same items load onto the same factors across groups or time). Metric invariance is achieved if differential responding to items are the same across groups or time (i.e., factor loadings are equal across groups or time). Scalar invariance is achieved if both the meaning of the construct and levels of the underlying items are the same across groups or time (i.e., both factor loadings and item intercepts/thresholds are equal across groups or time). If scalar invariance is met, differences in latent means and correlations across groups or time can be attributed to actual differences in the underlying construct, and not the result of measurement non-equivalence (i.e., measurement artifacts). If scalar invariance is not met, there may be evidence suggesting other construct-irrelevant factors are affecting the relationship between shame across study groups or time (Putnick, Diane & Bornstein, Mark, 2016).

Given that legal system-involved women with SUD particularly experience increased levels of stigmatization and may have higher levels of internalized shame, the present secondary analysis study aimed to examine the effects of two forms of treatment, relapse prevention (RP) and mindfulness-based relapse prevention (MBRP) on internalized shame outcomes for legal system-involved women in a residential addiction treatment setting (Meyers et al., 2021; Witkiewitz, Warner, et al., 2014). Through a series of confirmatory factor analytic models, we first tested the measurement invariance of the ISS (Cook, 1994) across treatment conditions and across time. Second, we examined whether there were changes in internalized shame in response to treatment.

Method

Participants and Procedures

Participants included in the current secondary analysis study were recruited to participate in a randomized clinical trial of mindfulness-based relapse prevention (MBRP) versus relapse prevention (RP) as treatments for SUD (Witkiewitz, Warner, et al., 2014) conducted from July 2010 through December 2011. Participants were adult women (N=105) who were receiving residential treatment services in a nonprofit residential SUD treatment center in the Pacific Northwest for women involved in the criminal legal system. Women who received services had previous arrests for drug use or possession, burglary, sex work, or other nonviolent crimes, with average years of incarceration of 2.46 years (SD = 2.02). Many women in the treatment program used multiple drugs, however the primary drugs of choice reported by participants included: methamphetamine (35.5%), heroin and other opioids (22.6%), cocaine (19.4%), alcohol (9.7%), cannabis (6.5%), nicotine (3.2%), and other drugs (3.2%). A chart review on a subset of participants (n = 86) indicated several of the women also experienced significant mental health problems, including histories of verbal, emotional or physical abuse (89.2%), chronic anxiety (73.5%), chronic depression (70.7%), history of severe trauma/PTSD (69.2%), and at least one prior suicide attempt (46.0%).

A full description of trial procedures is provided by Witkiewitz and colleagues (2014) and is summarized here. Participants were convicted on substance-related charges prior to treatment and underwent a detoxification and stabilization stage (approximately 21 days) prior to enrolling in the trial. At the end of the stabilization stage women were notified about the study by treatment staff. Women who expressed interest in participation met with research staff to learn more about the study, review inclusion criteria (English proficiency, willingness to be randomized, and ability to provide consent), provide informed consent to participate in the research if eligible, and complete a baseline assessment of self-report measures. A posttreatment assessment was conducted immediately following the 8-week treatment groups and a follow-up assessment was conducted approximately 15 weeks after MBRP or RP treatment ended (approximately six months after baseline assessment and after most women were discharged). The study was funded by a grant from Washington State University-Vancouver. The Washington State University-Vancouver Institutional Review Board approved all study procedures.

After completing the baseline assessment, participants were randomized into one of two group treatments that met twice weekly for fifty-minute sessions over eight weeks. The MBRP intervention was based on the MBRP manual (Bowen et al., 2011), adapted to a rolling group format. MBRP groups focused on increasing acceptance, awareness, and nonjudgment of present moment experience, identifying common reactions to triggers, and integrating mindfulness in daily life and to effectively response to triggers without automatically reacting. The RP intervention was based on prior RP manuals (Daley & Marlatt, 2006; Monti et al., 2002) and adapted to a rolling group format with the amount of material and homework in the MBRP intervention. RP groups focused on teaching participants to identify high-risk situations for substance use and training in coping skills for managing craving and high-risk situations. Master’s level clinicians employed by the treatment program and professionally trained in the treatments led the MBRP and RP groups.

Measures

All measures were completed by participants via self-report on paper and pencil forms with research staff available to answer questions. The current study used a slightly modified version of the 24-item Internalized Shame Scale (ISS) (Cook, 1994), which was administered at baseline, posttreatment, and the 15-week follow-up assessment. The ISS was developed to measure internalized shame with subscales measuring both internalized shame and self-esteem, but we did not administer the self-esteem subscale in the current study. The ISS used in the current study included 23 items from the shame subscale measured on a response scale that ranged from 1 “Never” to 7 “Always” with each item including statements that reflect “feelings and experiences” that may be common or familiar to the individual. Sample items include “I think people look down on me” and “I see myself as being very small and insignificant.” One item from the 24-item ISS, “When I compare myself to others I am just not as important,” was not included in the current study due to a clerical error. Internal consistency reliability of the measure, calculated using both McDonald’s Ω and Cronbach’s α, was excellent at all time points (baseline: Ω = .976 and α = .976; posttreatment: Ω = .977 and α = .977; follow-up: Ω = .968 and α = .969).

Statistical Analyses

All analyses were estimated in Mplus version 8.4 (Muthén & Muthén, 2019). Three individuals (2.9% of the sample) did not complete the ISS at any timepoints, thus the maximum sample size for analyses was 102. Of these individuals, 60 (58.8%) completed at least part of the ISS at posttreatment and 31 participants (30.4%) completed at least part of the ISS at the 15-week follow-up assessment. Model-based missing data procedures were used to estimate all models, sensitivity to missing data assumptions were tested using missing not at random models and alternative missing data handling strategies, and attrition analyses were conducted to assess for differences between those with complete data and those who were missing for all study variables.

Confirmatory factor analyses and measurement invariance tests.

We examined the item-level distributions of all ISS items to determine the most appropriate estimator to use for confirmatory factor analyses and invariance testing. Given that the ISS items are on a 7-point ordinal response scale and many were not normally distributed (i.e., most items are positively skewed and some items were multimodal), a diagonally weighted least squares (WLSMV) was used, which is appropriate for ordinal indicators that are not normally distributed (Li, 2016).

Given the limited psychometric testing of the ISS, we first conducted a confirmatory factor analysis to examine a one-factor model of all ISS items (Model 1). We used multigroup confirmatory factor analysis to examine measurement invariance at baseline between intervention and control groups, comparing fit statistics for configural (Model 2), metric (Model 3), and scalar invariant models (Model 4). The configural models require that all items load onto a single-factor across each group or at each time point, but factor loadings and item thresholds are freely estimated across groups or across time. The metric models constrain factor loadings to equality across groups or across time. The scalar models constrain both factor loadings and item thresholds to equality across groups or across time.

Next, we used parameter constraints to examine measurement invariance over time, comparing the model fit statistics across configural, metric, and scalar invariant models. For time invariance testing, residual covariances were specified for matching items over time (e.g., residual variances of item 1 at each time point were allowed to correlate with each other). Using all time points, we were able to compare configural (Model 5) and metric invariant models (Models 6). However, testing scalar invariance requires constraining item thresholds across time, and estimating item thresholds requires that each category is endorsed at each time point. Given our sample size, for multiple items, one or more categories (e.g., item 1 at post) were collapsed so that the same number of thresholds could be estimated across time (a score of 7 was recoded as a score of 6). To maximize statistical power and minimize collapsing of categories, we conducted the time invariance analyses separately between baseline and post (Models 7–9) as well as between baseline and follow-up (Models 10–12), collapsing the minimum number of categories across the minimum number of items for models to converge.

We conducted χ2 difference tests (using the DIFFTEST option in Mplus, Satorra & Bentler, 2010) to compare configural, metric, and scalar models. We also compared models using a decrease in CFI ≥.01 (Cheung & Rensvold, 2002) to indicate a significant decrement in model fit.

Latent growth curve models.

After establishing support for the reliability, factor structure, and measure invariance of the latent factor score at each time point and across treatment groups we created total scores on the ISS by summing all items of the ISS into a total score for each timepoint. A total score was used to reduce the total number of model parameters Maximum likelihood estimation was used to estimate change in ISS over time using a latent growth curve modeling framework. Maximum likelihood uses all available data to estimate the model and is a preferred method for handling missing data when data are missing, under the assumption that data are missing at random in the analytic model (Hallgren & Witkiewitz, 2013; Schafer & Graham, 2002; Witkiewitz, Falk, et al., 2014). Given only three timepoints we initially tested a linear growth curve model, however we also explored a nonlinear growth model given the pattern of change observed in the data with large reductions in ISS scores from baseline to posttreatment and then leveling off of ISS scores from posttreatment to the 15-month follow-up. Treatment was included as a covariate predictor of intercept and slope to test whether there were differences in ISS scores over time by treatment groups. We also estimated multiple group models to assess the degree of change in ISS scores within each treatment group. Finally, given the large amount of missing data, we also estimated all models using two different missing data models, maximum likelihood estimation and multiple imputation, and also tested the missing not at random assumption using pattern mixture models and selection models (Enders, 2011; Witkiewitz et al., 2012).

Results

The participants included in the current study (n=102) were on average 34.12 years old (SD = 9.36), and were 64.2% non-Hispanic and white, 17.9% Black or African American, 11.9% American Indian/Alaska Native, 4.5% Asian, and 1.5% Hispanic. There were missing data on age (n=17; 16.7%) and race or ethnicity (n=35; 34.3%) because records were not available or the participant refused to answer. Participants who completed the posttreatment assessment (n=60, 58.8%) and follow-up assessment (n=31, 30.4%) were not systematically different on demographics or ISS scores at baseline from those who dropped out of treatment or were lost to follow-up. Missing data at posttreatment and follow-up were prevalent for numerous reasons (e.g., no permanent phone or address, lost to follow-up, incarceration). Descriptive statistics for all included measures in the current secondary analysis are provided in Table 1, see (Witkiewitz, Warner, et al., 2014) for full outcome results.

Table 1.

Summary of Fit Indices for Confirmatory Factor Analysis and Measurement Invariance Testing Models

Model Description Model Fit Indices Model Comparison χ2 Difference Testing
CFI TLI RMSEA LB UB χ2 df SRMR χ2 df p
Model 1: 1-Factor CFA Baseline 0.963 0.959 0.129 0.117 0.141 620.07 230 0.053
Condition Invariance-Baseline CFI TLI RMSEA LB UB χ2 df SRMR χ2 df p
Model 2: Configural 0.971 0.968 0.126 0.112 0.140 756.54 418 0.062 Metric vs Configural 19.09 21 0.5797
Model 3: Metric 0.972 0.971 0.120 0.106 0.134 762.79 439 0.063 Scalar vs Configural 110.85 130 0.8868
Model 4: Scalar 0.977 0.981 0.098 0.083 0.111 814.56 548 0.064 Scalar vs Metric 90.91 109 0.8952
Time Invariance (all time points) CFI TLI RMSEA LB UB χ2 df SRMR χ2 df p
Model 5: Configural 0.965 0.963 0.045 0.038 0.051 2657.56 2205 0.129
Model 6: Metric 0.971 0.97 0.040 0.033 0.047 2625.24 2251 0.133 Metric vs Configural 49.36 46 0.3405
Time Invariance (Baseline, Posttreatment) CFI TLI RMSEA LB UB χ2 df SRMR χ2 df p
Model 7: Configural 0.964 0.962 0.065 0.057 0.072 1382.97 965 0.084 Metric vs Configural 45.24 22 0.0025
Model 8: Metric 0.964 0.962 0.064 0.057 0.072 1409.64 987 0.085 Scalar vs Configural 131.66 130 0.4429
Model 9: Scalar 0.966 0.968 0.059 0.052 0.067 1492.58 1095 0.085 Scalar vs Metric 96.13 108 0.7863
Time Invariance (Baseline, Follow-up) CFI TLI RMSEA LB UB χ2 df SRMR χ2 df p
Model 10: Configural 0.973 0.971 0.049 0.039 0.057 1199.33 965 0.118 Metric vs Configural 15.979 22 0.8169
Model 11: Metric 0.974 0.972 0.048 0.038 0.057 1219.35 987 0.119 Scalar vs Configural 100.75 105 0.5991
Model 12: Scalar 0.975 0.976 0.045 0.035 0.053 1289.34 1070 0.120 Scalar vs Metric 81.46 83 0.5274
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Confirmatory Factor Analysis and Measurement Invariance Tests

A confirmatory factor analytic model with all 23 items loading onto a single factor demonstrated adequate fit across most fit indices (see Table 1). At baseline, we found support for scalar invariance across MBRP and RP groups, demonstrated by non-significant χ2 difference tests as well as improvements in other fit statistics (e.g., CFI = .977 for scalar model vs. CFI = .971 for configural model), supporting our ability to compare ISS scores across treatment conditions.

Across all time points, we found support for metric invariance over time. With the collapsing of some categories described above for the purpose of constraining thresholds over time, we found support for scalar invariance over time. In the baseline-to-post model, the metric model fit significantly worse than the configural model based on the χ2 difference test, though the change in CFI was .000, which did not exceed the .01 threshold, and the scalar model demonstrated better fit on most fit indices, and the χ2 difference test revealed that the scalar model did not fit significantly worse than either the configural or metric models. In the baseline-to-follow-up model, both χ2 difference testing and CFI supported scalar invariance. Across all models, the more constrained, parsimonious model (i.e., the scalar model) evinced the best fit based on CFI, TLI, and RMSEA, supporting measurement invariance across intervention conditions as well as across time.

Latent Growth Curve Models

Latent growth curve models of the ISS total scores where then estimated over time with one model examining intercept and slope of ISS scores regressed on treatment condition and separate latent growth curve models of ISS scores within each treatment condition separately. Results from different missing data strategies, including maximum likelihood estimation, multiple imputation, selection models, and pattern mixture models, produced substantively similar results so we report the results from maximum likelihood estimation below.

First, we estimated a linear growth curve model with intercept and linear slope and also a nonlinear growth model with intercept and nonlinear slope, with intercept and slope regressed on treatment. The model with a linear slope provided an excellent fit to the data (χ2 (2)=2.35, p=.31; CFI = 0.99, TLI=0.97, RMSEA = 0.04 (90% CI: 0.00, 0.21), however there was a negative residual variance on the slope parameter. The model with a nonlinear slope with the basis coefficient for the last time period freely estimated also provided an excellent fit to the data and the latent variable covariance matrix was positive definite with no negative residual variances (χ2 (1)=1.06, p=.30; CFI = 0.99, TLI=0.99, RMSEA = 0.02 (90% CI: 0.00, 0.27).

Results from the nonlinear growth model regressed on treatment indicated a mean intercept (baseline ISS) of 57.34 (SE=5.03) and a mean slope that was negative and significantly different from zero (slope = −11.26 (SE=5.07), p = .03), indicating significant reductions in ISS scores over time. Treatment condition was not significantly related to intercept (β = −0.03; B (SE) = −1.75 (6.81), p=0.80) or nonlinear slope (β = −0.30; B (SE) = −5.46 (6.04), p=0.37). As shown in Figure 1, results from the nonlinear growth model by treatment groups indicated significant reductions in ISS scores over time in both treatment conditions, with larger reductions in MBRP (slope = −16.33 (SE=4.99), p = .001) than RP (slope = −10.85 (SE=4.73), p = .02).

Figure 1.

Figure 1.

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Sample and estimated means from latent growth model by treatment group

Discussion

The current study sought to assess measurement invariance of the Internalized Shame Scale (ISS) and to identify changes in shame over time among a sample of legal system-involved women receiving either MBRP or RP. Our results indicate a one-factor model demonstrated scalar invariance across treatment groups and across time, supporting the measurement equivalence of the ISS scores in our sample. Overall, our findings lend support for using the ISS to measure internalized shame in the context of SUD treatment.

We observed significant reductions in shame from pre- to post-treatment, which were largely maintained at the follow-up assessment, approximately 15 weeks following the end of treatment. There were no differences by treatment conditions, thus both MBRP and RP were associated with reductions in internalized shame over time. Although the reduction of internalized shame was slightly larger among individuals who received MBRP compared to RP, this difference was not statistically significant. These findings may be explained by several factors. Individuals feeling shame often report feeling isolated, and the group nature of both RP and MBRP treatments may have engendered an encouraging space and decreased feelings of isolation (Biancarelli et al., 2019). Additional research is needed to explore how different SUD treatments and distinct treatment components influence internalized shame.

The present study is one of the few studies examining internalized shame within a legal system-involved sample. One prior study among people with SUD enrolled in a residential program reported participants with prior involvement in the legal system actually reported less internalized shame and less stigma-related rejection than individuals without involvement in the legal system (Luoma et al., 2007). The findings in this study may have been due to a selection effect in that criminal justice involved participants were largely compelled to engage in treatment, whereas those who were not criminal justice involved sought treatment voluntarily, which may have been in part due to shame and regret over prior substance use.

Legal system-involved women generally experience more stigma than men (LeBel, 2012). Multiple qualitative studies have shed light on the various harms facing legal system-involved women seeking treatment for SUD as a consequence of stigma (Boeri et al., 2021; Howard, 2015). Specifically, the intersecting stigma of substance use and previous incarceration has been shown to increase the needs of women for health and social services while restricting their access to these services (van Olphen et al., 2009). Given the unique challenges facing legal system-involved women with SUD, special considerations are needed for this population.

Increased trait mindfulness has been associated with decreased shame, increased self-esteem, and increased wellbeing (Bajaj, Gupta, et al., 2016; Bajaj, Robins, et al., 2016; Chan & Leung, 2021). Multiple studies have noted benefits of mindfulness-based interventions on shame and psychological phenomena relating to shame such as self-criticism and self-compassion (Malouf et al., 2017; Morley & Fulton, 2020; Schanche et al., 2021; Woods & Proeve, 2014), and the current study showed that MBRP, as well as a cognitive-behavioral RP intervention, were both associated with reductions in internalized shame. Importantly, women in the residential treatment program in the RP condition also had access to daily mindfulness meditation sessions that occurred as part of the broader treatment program. It could be the case that women in RP also experienced reductions in shame through learning mindfulness outside of the RP group sessions.

Limitations and Future Directions

The current study is not without limitations. First, the small sample size and high attrition rates likely contributed to non-positive definite matrices in some of the analyses. We conducted missing data models to test the effects of missing data, and found evidence that results were consistent across different missing data models, however future research should attempt to retain a larger sample for assessing changes in internalized shame over time. Additionally, our sample size was underpowered to assess statistical differences between treatment groups. A post-hoc Monte Carlo simulation indicated our study would need a sample of roughly 650 participants for power of 0.8 or greater to detect a significant effect between the MBRP and RP on change in ISS scores over time. Second, some of the women were in controlled environments (e.g., residential treatment) during the follow-up period, which was not considered in the analyses. Third, the MBRP and RP treatment groups were only one part of the overall treatment program, and all participants received a range of other treatment modalities during the residential treatment program, including daily mindfulness sessions. We did not have measures of hours of treatment received or what other treatments may have been received. Further, we used a total score, rather than the confirmatory factor models, to assess change over time, which may limit the reliability of the measure. Lastly, one item of the ISS was omitted due to clerical error, however the measure appears robust despite the missing item.

Yet, the current study also contributes to the growing literature of shame in the context of substance use by testing the assumption of measurement invariance among a widely used instrument. Further, this is the first study, to the authors knowledge, testing the psychometric properties and measurement invariance of the ISS, thus broadening the applicability of the measure. Importantly, our study assessed internalized shame and we did not have measures of stigma. Stigma is generally defined as a negative social attitude toward a characteristic of a person that has been devalued by society (Luoma et al., 2007). The stigmatizing beliefs often result in shame, the core emotion involved in internalized stigma, a response that limits social engagement and is associated with deleterious substance use outcomes. Limited evidence exists on the psychometrics of shame measures in the context of substance use and, therefore, additional studies are needed to examine and develop more effective ways of measuring shame in the context of substance use.

Future research should also assess stigma and shame in other treatment settings. There are many facets of stigma that negatively impact substance use treatment outcomes including anticipated, public, and systemic stigma (Crapanzano et al., 2018). However, the majority of measures target shame or internalized shame. Therefore, assessments are needed to capture additional forms of stigma operating at different levels of analysis. Additional work is also needed to better understand the associations between stigma related to substance use as compared to the stigma related to the criminal legal system involvement.

Internalized shame negatively impacts each step of the recovery continuum. Understanding the effects of shame among persons receiving SUD treatment for substance use is an important challenge in the field. Our analysis aimed to test the assumption of measurement invariance of one measure of internalized shame among legal system-involved women receiving treatment for SUD. Results indicate the ISS was measurement invariant across treatment groups and over time, and that internalized shame, as measured by the ISS, decreased over time during SUD treatment.

Funding

The study was funded by a grant from Washington State University-Vancouver (PI: Witkiewitz). VWJ was supported by a training grant (T32AA018108) from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and a diversity supplement (UH3DA051241) from the National Institute on Drug Abuse. DKR was supported by an individual training grant (F32AA028712) from the NIAAA. MRP was supported by a career development grant (K01AA023233) from the NIAAA. NIAAA and NIDA had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

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