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      Assessing a Faith-Based Program for Trauma Healing Among Jail Inmates: A Quasi-Experimental Study

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          Abstract

          This paper assesses a faith-based, short-term program for trauma healing among incarcerated individuals, “Correctional Trauma Healing Program” (CTHP). We hypothesized that participation in the CTHP would reduce negative consequences of lifetime trauma: symptoms of PTSD, state depression, state anger, suicidal ideation, and the risk of interpersonal aggression. We also hypothesized that the reduction, if found, would be partly attributable to anticipated program outcomes (a decrease in vengefulness and an increase in religiosity, forgiveness, perceived forgiveness of God, gratitude to God, and perceived positive impact of the Bible). To test our hypotheses, we conducted a quasi-experimental study of 349 jail inmates in Virginia. Manifest-variable structural equation modeling was applied to analyze data from pretest and posttest surveys. As hypothesized, the CTHP reduced the negative consequences of trauma by increasing religiosity and other positive attributes and decreasing vengefulness directly and/or indirectly via increased religiosity. Implications and limitations of our study are discussed.

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          Most cited references42

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          Diagnostic and Statistical Manual of Mental Disorders

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            The CES-D Scale: A Self-Report Depression Scale for Research in the General Population

            L Radloff (1977)
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              Missing data analysis: making it work in the real world.

              This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normal-model multiple imputation (MI) and maximum likelihood methods. Practical missing data analysis issues are discussed, most notably the inclusion of auxiliary variables for improving power and reducing bias. Solutions are given for missing data challenges such as handling longitudinal, categorical, and clustered data with normal-model MI; including interactions in the missing data model; and handling large numbers of variables. The discussion of attrition and nonignorable missingness emphasizes the need for longitudinal diagnostics and for reducing the uncertainty about the missing data mechanism under attrition. Strategies suggested for reducing attrition bias include using auxiliary variables, collecting follow-up data on a sample of those initially missing, and collecting data on intent to drop out. Suggestions are given for moving forward with research on missing data and attrition.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                International Journal of Offender Therapy and Comparative Criminology
                Int J Offender Ther Comp Criminol
                SAGE Publications
                0306-624X
                1552-6933
                July 14 2022
                : 0306624X2211108
                Affiliations
                [1 ]Baylor University, Waco, TX, USA
                [2 ]Seoul National University, Republic of Korea
                Article
                10.1177/0306624X221110804
                3387110b-45a1-41f4-9f20-6d852495819b
                © 2022

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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