The Effectiveness of Counseling in Boosting Adjustment Behaviour among Sodo and Tarcha Prisons

The Effectiveness of Counseling in Boosting Adjustment Behaviour among Sodo and Tarcha Prisons | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effectiveness of Counseling in Boosting Adjustment Behaviour among Sodo and Tarcha Prisons Behailu Beshir Seba, Abera Kumalo, Eyasu Gambura This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9484453/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the context of prison, counseling refers to a therapeutic process aimed at intervening therapeutically with individuals, primarily offenders, to address various personal, emotional, and psychological challenges. A study was conducted at Sodo and Tarcha prisons in Ethiopia to examine the effectiveness of counseling in boosting adjustment behavior among prisoners. This study employed a randomized controlled trial (RCT) with a pretest-posttest control group design. Consequently, 38 prisoners from Tarcha prison were assigned to the experimental group, while 41 prisoners from Sodo prison were assigned to the control group using the purposive sampling method. Bell's Adjustment Inventory was used to measure the adjustment level. Participants in the experimental group received group counseling, whereas the control group received no counseling at all. According to the results of the paired t-test, the adjustment level among prisons in the experimental group considerably decreased following group counseling (P<0.000), whereas no change was observed in the control group. Likewise, a simple linear regression analysis indicates a strong positive relationship between counseling and adjustment (R = 0.814). R Square (0.663) means 66.3% of the variability in adjustment is accounted for by counseling. Moreover, the one-way ANOVA results indicate a statistically significant difference among treatment completed, no counseling and dropped out early groups on the outcome variable, F (2, 88) = 93.31, p < .001. This study concludes that counseling is effective in enhancing adjustment behavior among prisoners. COUNSELING ADJUSTMENT BEHAVIOR PRISONER PRISON Introduction According to (Van Voorhis and Salisbury 2016), counseling is a therapeutic process used in prison settings that aims to intervene therapeutically with individuals, primarily offenders, to address a variety of personal, emotional, and psychological challenges. Likewise, it can help people who are having trouble adjusting emotionally. Counselling provides a nonjudgmental setting in which people can communicate their emotions, worries, and anxieties regarding the process of adjustment (Zamble and Porporino 2013). Without the structured environment and support networks offered by the prison system, adjusting to life outside of prison can be challenging (Anderson‐Facile 2009). Finding housing, finding work, and re-establishing relationships with friends and family are all part of the daunting process of reintegrating into society, which many people find difficult (Travis 2005). Studies show that a sizable portion of prisoners in Ethiopia experience depression and other mental health conditions, raising concerns about the prevalence of mental illnesses among this population. However, little is known about the effectiveness of counseling services in Ethiopia, and further research is needed to identify the specific ways that counseling can work best in this context (Yesuf, Birhan et al. 2022). The challenges of incarceration, such as the loss of family and resources, may exacerbate these conditions. Counselling services have been found to be beneficial in improving the mental health of prisoners and promoting positive adjustment behavior. 66.1% of prisoners use guidance and counseling services, according to a recent cross-sectional study done in Northwestern Ethiopia. This suggests that these services could be a valuable tool for enhancing the wellbeing of prisoners in Ethiopia. Additionally, this study found factors linked to inmates' use of healthcare services, offering insights into how to make counseling services more accessible to those in need (Yesuf, Birhan et al. 2022). Repeat offending, also known as criminal recidivism, in people released from prison has remained high over many decades. To address this, psychological treatments have been increasingly used in criminal justice settings; however, there is little evidence about their effectiveness (Beaudry, Yu et al. 2021). According to (Fair and Walmsley 2024), there were 110,000 prisons in Ethiopia in 2024. Sodo and Tarcha prison institutions are among the regional prisons which provide counseling service for inmates. Nevertheless, the data from the institutions revealed that the numbers of prisoners coming to prison are increasing over time. This is a gap in the literature and needs to be addressed. Researchers of this study are interested to study the effectiveness of counseling in boosting adjustment behavior among Sodo and Tarcha prisoners in Ethiopia. 1.1. Objectives of the study 1.1.1. General objectives : To determine whether counseling has a positive impact on prisoners' behavior among Sodo and Tarcha prisoners in Ethiopia. 1.1.2. Specific Objectives The specific objectives of the study are to: 1. Examine any behavioral alterations brought on by counseling. 2. Determine the degree of adjustment between the control and experimental groups. 3. Investigate the degree of adjustment among treatment completed, no counseling and dropped out early groups. Methods This study employed a randomized controlled trial (RCT) with a pretest-posttest control group design to determine the effectiveness of counseling in enhancing prisoners' adjustment behavior. The data were analyzed using percentages, paired t- tests, ANOVA and simple linear regression. Three rounds of data collection were conducted. Initially, Bell's Adjustment Inventory was administered to all prisoners listed in the institution database with a history of adjustment problems. Based on the result, prisoners adjustment problem were assigned into the control and experimental group. Then, prior to the group counseling process, the pre-test results were administered and scored. Finally, at the end of the counseling process, the post-test was administered and scored using the same tool. Research is a process of systematic inquiry that entails the collection of data, documentation of critical information, and analysis and interpretation of that data/information, in accordance with suitable methodologies set by specific professional fields and academic disciplines (Hancock, Ockleford et al. 2001). This study is concerned with assessing the effectiveness of counseling in enhancing adjustment behavior among Sodo and Tarcha prisoners in Ethiopia. 2.1. Participant characteristics The data base of the institutions showed that 114 prisoners in Sodo prison were found to be poorly adjusted whereas, 96 prisoners in Tarcha prison have adjustment problems. Bell's Adjustment Inventory was administered to all poorly adjusted prisoners in both institutions. Based on the result, 41 prisoners from Sodo prison institution have adjustment problems. On the other hand, 50 prisoners of Tarcha prison were discovered to be ill-adjusted. Therefore, using a purposive sampling method, 41 prisoners in Sodo prison were assigned to the control group. Following attrition due to illness and release from prison , 12 prisoners dropped out of the treatment group (N=50). The researchers then limited the study to the remaining 38 prisoners in Sodo prison. This is considered as the target population of the study. 2.2. Sampling procedures The target population for this study comprised prisoners with history of adjustment and incarcerated at Sodo and Tarcha prisons during the data collection period. This study has three groups of participants; experimental, control and drop out groups. The researchers used purposive sampling technique to obtain the total of 91 participants from both prisons with history of adjustment problems. Participants were divided into three groups based on counseling receipt and completion status. 2.3. Study Site The study was carried out at the Tarcha and Sodo Prison Institutions in Ethiopia. The Dawro Zone is located roughly 319 kilometres from Hawassa, the capital of the Southern Nations, Nationalities, and Peoples' Region (SNNPR), and 500 kilometres southwest of Addis Ababa, the capital of Ethiopia. Gamo Gofa Zone borders it on the south, Konta special woreda on the west, the Gojeb River and Oromia Region on the north, Hadiya and Kembata Tembaro Zones on the northeast, and Wolayita Zone on the east. Tarcha town serves as the Dawro Zone's administrative hub (Utino, Birhanu et al. 2023). Wolaita Zone is named after the Wolaita people, whose homeland is in the zone. It is bordered by Gamo Gofa Zone to the south, the Omo River to the west (which separates it from Dawro Zone), Kembata Tembaro to the northwest, Hadiya to the north, the Oromia Region to the northeast, the Bilate River (which separates it from Sidama Region) to the east, and Lake Abaya (which separates it from Oromia Region) to the southeast. The administrative center of Wolaita Zone is Sodo. Both towns have one prison institution. According to the data base of Sodo and Tarcha prison institutions currently there are 114 and 96 prisoners who have adjustment problems respectively. 2.4. Instruments In this study, the researchers used one questionnaire and Inventory to collect data. These are Background information and Bell Adjustment Inventory. 2.4.2. Background information’s includes socio-demographic characteristics such as sex and age. 2.4.3. Bells Adjustment Questionnaire : It is a widely used adjustment inventory and it was developed by H. M. Bell in the year 1934. The inventory measures adjustment in four different areas – home, health, social, and emotional separately and also yields a composite score for overall adjustment. Each area has 10 items which can be answered by yes or no response. Its reliability ranges from 0.70 to 0.92 and validity is also very high (Sinha 2010). 2.5. Procedure First, the prison authority's administrative approval and ethical clearance were obtained. From the institutional data, these researchers chose inmates who matched the goals of the current study. After completing this, a date is set for initiating communication with the study's participants. Prior to the individuals' participation in the aforementioned research project, they provided written and signed consent. Before the treatment begins, participants were asked for their permission to complete the self-report questionnaire for Bell Adjustment Inventory. The result was kept confidential and opened only after post-test evaluation. The counselor briefly described the group's purpose, the number of sessions, how the group members interacted with each other and the therapist. Furthermore, the therapist also described briefly about the topics to be covered, interventions to be introduced, and any follow-up on homework assignments. A Cognitive-Behavioral Therapy (CBT) group model was used. CBT is one of the most rigorously researched therapeutic models and has proven effective for a wide range of issues common among prisoners, including anger, aggression, depression, anxiety, and antisocial cognitions (Lipsey, Landenberger et al. 2007). The following standardized protocol was used: Structure of the 10-Week Protocol: · Session 1: Group Introduction & Building Trust. Establishing group rules, goals, and confidentiality. · Session 2: The ABC Model. Introducing the link between thoughts and feelings/behaviors. · Session 3: Identifying Criminal & Negative Thinking Patterns. · Session 4: Cognitive Restructuring: Challenging and Changing Thoughts. · Session 5: Anger & Trigger Identification. · Session 6: Anger Management & Emotion Regulation Skills. · Session 7: Problem-Solving Skills. · Session 8: Pro-social Communication & Empathy Training. · Session 9: Relapse Prevention: Applying skills to future challenges inside and outside prison. · Session 10: Review, Termination, and Future Goals. After completing group therapy, it can be said that participants are better able to relate to others, become aware of their own feelings and needs and express them, offer and receive support, learn new interpersonal skills they can use every day, and develop a greater awareness of themselves through sincere feedback from others. The group therapy sessions were run by the researchers. The intervention lasted for 10 weeks with 1 session weekly. Immediately after group counseling, the questionnaire booklet was given to each participant and post-test was administered and scored by using the same tools. SPSS version 20 was used for data analysis. Percentage, paired t-test, ANOVA and simple linear regression were used to reveal the effects of counseling in boosting adjustment behavior among prisoners. At the end of the treatment, participants were thanked for their participation in the research practice and were instructed to keep themselves away from criminal activity. They were also informed that the counselors are ready to help participants in the study for any difficulty that might happen in the future. 2.6. Research Hypothesis This study tested the following research hypothesis: 2.6.2. H 1 : There will be statistically significant difference in the adjustment level of experimental group, which results due to the treatment effect. 2.6.3. H 1 : There will be statistically significant difference among treatment completed, no counseling and dropped out early groups on the outcome variable. 2.6.4. H 1 : Counseling has a significant positive effect on adjustment, accounting for a substantial proportion of the variability in adjustment outcomes. 2.7. Inclusion Criteria To be eligible for this study, participants met all of the following criteria: · Be an incarcerated adult male or female at Sodo or Tarcha Prison. · Adult prisoners (18 years or older) in Sodo or Tarcha prison. · Fluent in the language of the counseling (e.g., Amharic). · Those who were held in high-security segregation and participated in a similar cognitive-behavioral program within the past year were excluded. · Capable of understanding and providing voluntary, informed consent. 2.8. Ethical Consideration The goal of the study was briefly explained to each responder before the questionnaire was given out. The study was conducted with the subjects' consent, and the responses' confidentiality was maintained throughout. The involvement of the respondents was voluntary, and Wolaita Sodo University requested written consent from the Sodo and Tarcha Prison Institutions. Cultural customs and individual privacy were honored. 2.9. Data Collection Background information’s and Bell Adjustment Inventory are data collecting tools used in this study. Both the Inventory and the questionnaire were prepared in Amharic language. For that matter, language experts and Psychologists were consulted to avoid language problems. Finally, the Amharic version of the questionnaire was distributed to all participants in the study and data was collected properly. These researchers assisted participants when needed or provided further explanation when requested by participants about each items in both the Inventory and the questionnaires. 2.10. Data Analyses To analyze data, the researcher used quantitative statistics. In order to accomplish this, 91 participants were selected that match the purpose of this research. Throughout the process of analysis, statistical package for social sciences (SPSS) version 20.00 was used. All significance tests were made at α = 0.05. The data was visually checked up, organized and coded in a computer Excel for analysis. Then, the result was presented in the form of tables, texts, frequencies, percentage and summary statistics such as mean, standard deviation, correlation, ANOVA, simple linear regression and pared t-test to describe the study population in relation to relevant variables. Results The major purpose of the present study was to examine the effectiveness of counseling in boosting adjustment behaviour among prisoners. This study included 38 prisoners in experimental, 41 prisoners in control and drop out groups. Totally, the intervention lasted for 10 weeks with 1 session weekly. Table 1 Sex and Age Distribution of Experimental, Control and Drop out Groups Groups Sex Age Male Female Total 45 Total Experimental 27 11 38 15 21 2 0 38 Control 38 3 41 4 16 20 1 41 Drop out 8 4 12 7 4 1 0 12 Total 73 18 91 26 41 23 1 91 As shown in the table, of the 38 participants in the experimental group, 27 were male and 11 were female, indicating relatively better female representation compared to the other groups. In contrast, the control group was highly male-dominated, with 38 males and only 3 females out of 41 participants. The dropout group consisted of 8 males and 4 females, suggesting a slightly more balanced distribution than the control group, although males still formed the majority. Regarding age distribution, the experimental group was predominantly younger, with 15 participants below 22 years and 21 participants between 23–33 years, while only 2 participants fell within the 34–44 age range and none were above 45. In the control group, 4 participants were below 22 years, 16 were aged 23–33, 20 were between 34–44, and 1 participant was above 45, indicating a relatively older age composition compared to the experimental group. The dropout group was mainly composed of younger individuals, particularly those below 22 years. Table 2 Paired t-test of BAS Score in the Pre and Post-test in the Control Group Variable Group Test Mean N SD t-value P-value Home Sodo Prison Pre-test 4.80 41 2.58 .132 .896 Post-test 4.73 41 2.42 Social Sodo Prison Pre-test 6.51 41 1.95 1.035 .307 Post-test 6.02 41 2.16 Emotion Sodo Prison Pre-test 7.20 41 1.94 1.722 .093 Post-test 6.32 41 2.71 Health Sodo Prison Pre-test 5.10 41 2.37 1.616 .114 Post-test 4.22 41 2.40 A paired-samples t-test was conducted to examine differences in BAS scores between pre-test and post-test measurements among participants in the control group at Sodo Prison. The above table indicates that there is no statistically significant mean difference between the two mean scores of Adjustment scale (Home P = 0.896; Social P = 0.307; Emotion P = 0.093; Health P = 0.114) of control groups before and after group counseling. This indicates that, in the absence of any intervention, participants’ adjustment levels remained relatively stable. Table 3 Paired t-test of BAS score in the Pre and Post-test in the Experimental Group Variable Group Test Mean N SD t-value P-value Home Tarcha Prison Pre-test 24.68 38 4.45 33.054 .000 Post-test 1.45 38 1.43 Social Tarcha Prison Pre-test 26.32 38 5.44 27.382 .000 Post-test 1.32 38 1.23 Emotion Tarcha Prison Pre-test 25.84 38 4.81 28.126 .000 Post-test 3.61 38 .79 Health Tarcha Prison Pre-test 25.45 38 4.56 30.963 .000 Post-test 3.03 38 1.05 A paired-samples t-test was conducted to assess the effect of the intervention on BAS scores among participants in the experimental group at Tarcha Prison. As shown in Table 3, there is statistically significant mean difference between the two mean scores of adjustment scale (Social P = 0.000; Emotion P = 0.000; Health P = 0.000) of experimental groups before and after group counseling. It is believed by the researchers to have been so due to the treatment effect and not by chance As shown in Table 4, a simple linear regression analysis was conducted to examine the effect of counseling on adjustment. The model summary indicates a strong positive relationship between counseling and adjustment (R = 0.814). R Square (0.663) means 66.3% of the variability in adjustment is accounted for by counseling. The ANOVA results reveal that the overall regression model is statistically significant (F (1, 77) = 151.394, p < 0.001). This indicates that counseling is a significant predictor of adjustment. As shown in Table 5, the one-way ANOVA results indicate a statistically significant difference among the three groups on the outcome variable, F (2, 88) = 93.31, p < .001. Specifically, participants who completed the full treatment protocol reported a substantially lower mean score (M = 9.39, SD = 4.00) compared to both the no counseling group (M = 23.93, SD = 5.84) and those who dropped out early (M = 24.50, SD = 5.13). Post hoc comparisons further revealed that these differences were statistically significant, with the completed treatment group scoring significantly lower than both the no counseling group (Mean Difference = -14.53, p < .001) and the dropout group (Mean Difference = -15.11, p < .001). However, no significant difference was found between the no counseling and dropout groups (Mean Difference = -0.57, p = .730). Overall, the findings suggest that completing the full treatment protocol was associated with significantly better adjustment (i.e., fewer imprisonments) compared with both the no-counseling and early-dropout groups. Discussion This study shows that counselling significantly improves inmates' adjustment behaviour. The results demonstrated that, in comparison to inmates who did not receive counselling, those who did were better able to adapt emotionally, socially, and in terms of their health. The counselling and non-counseling groups showed statistically significant differences, suggesting that counselling significantly enhances inmates' capacity to adjust both inside the prison and at home after release. This is consistent with a systematic review and meta-analysis of randomized controlled trials, counseling can be effective in addressing the psychological impact of incarceration and reducing the risk of reoffending among recently released individuals. The study suggested that counseling helps a lot to development coping skills, emotional regulation, and social support, which are all important factors in promoting positive adjustment outcomes (Beaudry, Yu et al. 2021 ). Another research study focused specifically on the effectiveness of counseling in promoting positive adjustment behavior found that counseling interventions were successful in helping individuals develop adaptive coping strategies, improve emotional regulation, enhance self-esteem, and foster healthier relationships. These positive adjustments were observed across various domains of life, including personal, social, and occupational functioning (Elliott, Bohart et al. 2011 ). In terms of psychological therapy, Cognitive-Behavioral Therapy (CBT) has been shown to significantly improve psychological adjustment among inmates, suggesting that tailored counseling approaches can effectively address the adjustment challenges faced by prisoners. The positive effects of therapy on psychological adjustment underscore counseling's vital role in helping prisoners cope with incarceration stressors and prepare for reintegration into society, as emphasized in research by (Haney 2001 ). Despite these positive findings, it's crucial to be aware of potential drawbacks including variations in the length, nature, and demographics of counselling, which have the potential to affect the extent of progress in adjustment. To further improve the rehabilitation outcomes of inmates, future study should examine the long-term impacts of counselling and incorporate multimodal psychological therapies. Likewise, there are still many barriers to providing adequate mental health care to incarcerated individuals. These barriers include a lack of resources, stigma surrounding mental health, and a lack of trained mental health professionals (James and Glaze 2016 ). In conclusion, counseling is an effective intervention for boosting adjustment behavior among prisoners, significantly contributing to positive adjustment. This underscores the need for systematic integration of counseling services in prison rehabilitation programs to foster inmates' successful adjustment and societal reintegration. Therefore, based on the findings of the present research the researchers accepted the hypothesis. Conclusion From the findings of the study, it can be concluded that counseling service is very effective and leads to adjustment behavior once prisoners are released from prison. Furthermore, the study also revealed that counseling received prisoners who got imprisonment once had better adjustment when compared with other counterparts. Finally, it can be concluded that this finding can serve as a stepping-stone for further future studies. Recommendations It is recommended therefore that: Counseling service offered in prisons should be encouraged to suit the current needs. The community in both institutions should work together in harmony to bring long lasting change in prisoner’s life. Declarations Declaration of conflicting interests No potential conflicts of interest were disclosed by the author(s) with regard to the research, writing, or publication of this paper. Acknowledgements We acknowledge the participants who shared their experiences. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. References Anderson-Facile, D. (2009). Basic challenges to prisoner reentry. Sociology Compass , 3 (2), 183–195. Beaudry, G., Yu, R., Perry, A. E., & Fazel, S. (2021). Effectiveness of psychological interventions in prison to reduce recidivism: A systematic review and meta-analysis of randomised controlled trials. The Lancet Psychiatry , 8 (9), 759–773. Elliott, R., Bohart, A. C., Watson, J. C., & Greenberg, L. S. (2011). Empathy Psychotherapy 48(1): 43. Fair, H., & Walmsley, R. (2024). World prison population list, ICPR. Hancock, B., Ockleford, E., & Windridge, K. (2001). An introduction to qualitative research, Trent focus group London. Haney, C. (2001). The psychological impact of incarceration: Implications for post-prison adjustment . Urban Institute. James, D. J., & Glaze, L. E. (2016). Mental health problems of prison and jail inmates. Lipsey, M. W., Landenberger, N. A., & Wilson, S. J. (2007). Effects of cognitive-behavioral programs for criminal offenders. Campbell systematic reviews , 3 (1), 1–27. Sinha, S. (2010). Adjustment and mental health problem in prisoners. Industrial psychiatry journal , 19 (2), 101–104. Travis, J. (2005). But they all come back: Facing the challenges of prisoner reentry . The Urban Insitute. Utino, L., Birhanu, B., Getachew, N., & Ereso, B. M. (2023). Perceived quality of medical services at outpatient department of public hospitals in Dawro Zone, Southern Ethiopia. BMC Health Services Research , 23 (1), 209. Van Voorhis, P., & Salisbury, E. (2016). Correctional counseling and rehabilitation . Routledge. Yesuf, Y. M., Birhan, A. A., Birara, A. G., Adimas, B. D., Bezabh, A. B., & Agmase, N. G. (2022). Prevalence and correlates of mental illness among inmates in North-western Ethiopia: A new look into the roles of rehabilitation service use. Frontiers in Psychiatry , 13 , 983355. Zamble, E., & Porporino, F. J. (2013). Coping, behavior, and adaptation in prison inmates . Springer Science & Business Media. Table 5 Table 5 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files ANOVAOutput2.spv LogisticRegression1.sav PairedttestControlgroup1.sav PairedttestExperimentalgroup4.sav Table5.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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and Salisbury 2016), counseling is a therapeutic process used in prison settings that aims to intervene therapeutically with individuals, primarily offenders, to address a variety of personal, emotional, and psychological challenges. Likewise, it can help people who are having trouble adjusting emotionally. Counselling provides a nonjudgmental setting in which people can communicate their emotions, worries, and anxieties regarding the process of adjustment (Zamble and Porporino 2013). Without the structured environment and support networks offered by the prison system, adjusting to life outside of prison can be challenging (Anderson‐Facile 2009). Finding housing, finding work, and re-establishing relationships with friends and family are all part of the daunting process of reintegrating into society, which many people find difficult (Travis 2005).\u003c/p\u003e\n\u003cp\u003eStudies show that a sizable portion of prisoners in Ethiopia experience depression and other mental health conditions, raising concerns about the prevalence of mental illnesses among this population. However, little is known about the effectiveness of counseling services in Ethiopia, and further research is needed to identify the specific ways that counseling can work best in this context (Yesuf, Birhan et al. 2022). The challenges of incarceration, such as the loss of family and resources, may exacerbate these conditions. \u0026nbsp;Counselling services have been found to be beneficial in improving the mental health of prisoners and promoting positive adjustment behavior. 66.1% of prisoners use guidance and counseling services, according to a recent cross-sectional study done in Northwestern Ethiopia. This suggests that these services could be a valuable tool for enhancing the wellbeing of prisoners in Ethiopia. \u0026nbsp;Additionally, this study found factors linked to inmates\u0026apos; use of healthcare services, offering insights into how to make counseling services more accessible to those in need (Yesuf, Birhan et al. 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRepeat offending, also known as criminal recidivism, in people released from prison has remained high over many decades. To address this, psychological treatments have been increasingly used in criminal justice settings; however, there is little evidence about their effectiveness (Beaudry, Yu et al. 2021). According to (Fair and Walmsley 2024), there were 110,000 prisons in Ethiopia in 2024. Sodo and Tarcha prison institutions are among the regional prisons which provide counseling service for inmates. Nevertheless, the data from the institutions revealed that the numbers of prisoners coming to prison are increasing over time. This is a gap in the literature and needs to be addressed. Researchers of this study are interested to study the effectiveness of counseling in boosting adjustment behavior among Sodo and Tarcha prisoners in Ethiopia. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1. Objectives of the study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.1. General objectives\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine whether counseling has a positive impact on prisoners\u0026apos; behavior among Sodo and Tarcha prisoners in Ethiopia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.2.\u0026nbsp; \u0026nbsp;Specific Objectives\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe specific objectives of the study are to:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Examine any behavioral alterations brought on by counseling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Determine the degree of adjustment between the control and experimental groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Investigate the degree of adjustment among treatment completed, no counseling and dropped out early groups.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\n\u003cp\u003eThis study employed a \u003cstrong\u003erandomized controlled trial (RCT) with a pretest-posttest control group design\u003c/strong\u003e to determine the effectiveness of counseling in enhancing prisoners\u0026apos; adjustment behavior. The data were analyzed using percentages, paired t- tests, ANOVA and simple linear regression. Three rounds of data collection were conducted. Initially, Bell\u0026apos;s Adjustment Inventory was administered to all prisoners listed in the institution database with a history of adjustment problems. Based on the result, prisoners adjustment problem were assigned into the control and experimental group. Then, prior to the group counseling process, the pre-test results were administered and scored. Finally, at the end of the counseling process, the post-test was administered and scored using the same tool.\u003c/p\u003e\n\u003cp\u003eResearch is a process of systematic inquiry that entails the collection of data, documentation of critical information, and analysis and interpretation of that data/information, in accordance with suitable methodologies set by specific professional fields and academic disciplines (Hancock, Ockleford et al. 2001). This study is concerned with assessing the effectiveness of counseling in enhancing adjustment behavior among Sodo and Tarcha prisoners in Ethiopia. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.1. \u003c/strong\u003e\u003cstrong\u003eParticipant characteristics \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data base of the institutions showed that 114 prisoners in Sodo prison were found to be poorly adjusted whereas, 96 prisoners in Tarcha prison have adjustment problems. Bell\u0026apos;s Adjustment Inventory was administered to all poorly adjusted prisoners in both institutions. Based on the result, 41 prisoners from Sodo prison institution have adjustment problems. On the other hand, 50 prisoners of Tarcha prison were discovered to be ill-adjusted. \u003cem\u003eTherefore, using \u003c/em\u003e\u003cstrong\u003e\u003cem\u003ea\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e purposive sampling method, 41 prisoners in Sodo prison were assigned to the control group. \u003c/em\u003eFollowing attrition due to illness and release from prison\u003cem\u003e, 12 prisoners dropped out of the treatment group (N=50). The researchers then limited the study to the remaining 38 prisoners in Sodo prison.\u003c/em\u003e\u003cem\u003e \u003c/em\u003eThis is considered as the target population of the study. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Sampling procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe target population for this study comprised prisoners with history of adjustment and incarcerated at Sodo and Tarcha prisons during the data collection period. This study has three groups of participants; experimental, control and drop out groups. The researchers used purposive sampling technique to obtain the total of 91 participants from both prisons with history of adjustment problems. Participants were divided into three groups based on counseling receipt and completion status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Study Site\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was carried out at the Tarcha and Sodo Prison Institutions in Ethiopia. The Dawro Zone is located roughly 319 kilometres from Hawassa, the capital of the Southern Nations, Nationalities, and Peoples\u0026apos; Region (SNNPR), and 500 kilometres southwest of Addis Ababa, the capital of Ethiopia. Gamo Gofa Zone borders it on the south, Konta special woreda on the west, the Gojeb River and Oromia Region on the north, Hadiya and Kembata Tembaro Zones on the northeast, and Wolayita Zone on the east. Tarcha town serves as the Dawro Zone\u0026apos;s administrative hub (Utino, Birhanu et al. 2023).\u003c/p\u003e\n\u003cp\u003eWolaita Zone is named after the Wolaita people, whose homeland is in the zone. It is bordered by Gamo Gofa Zone to the south, the Omo River to the west (which separates it from Dawro Zone), Kembata Tembaro to the northwest, Hadiya to the north, the Oromia Region to the northeast, the Bilate River (which separates it from Sidama Region) to the east, and Lake Abaya (which separates it from Oromia Region) to the southeast. The administrative center of Wolaita Zone is Sodo. Both towns have one prison institution. According to the data base of Sodo and Tarcha prison institutions currently there are 114 and 96 prisoners who have adjustment problems respectively. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the researchers used one questionnaire and Inventory to collect data. These are Background information and Bell Adjustment Inventory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2. Background information\u0026rsquo;s\u003c/strong\u003e includes socio-demographic characteristics such as sex and age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.3. Bells Adjustment Questionnaire\u003c/strong\u003e: \u003c/p\u003e\n\u003cp\u003eIt is a widely used adjustment inventory and it was developed by H. M. Bell in the year 1934. The inventory measures adjustment in four different areas \u0026ndash; home, health, social, and emotional separately and also yields a composite score for overall adjustment. Each area has 10 items which can be answered by yes or no response. Its reliability ranges from 0.70 to 0.92 and validity is also very high (Sinha 2010). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Procedure \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, the prison authority\u0026apos;s administrative approval and ethical clearance were obtained. From the institutional data, these researchers chose inmates who matched the goals of the current study. After completing this, a date is set for initiating communication with the study\u0026apos;s participants. Prior to the individuals\u0026apos; participation in the aforementioned research project, they provided written and signed consent. Before the treatment begins, participants were asked for their permission to complete the self-report questionnaire for Bell Adjustment Inventory. The result was kept confidential and opened only after post-test evaluation. The counselor briefly described the group\u0026apos;s purpose, the number of sessions, how the group members interacted with each other and the therapist. Furthermore, the therapist also described briefly about the topics to be covered, interventions to be introduced, and any follow-up on homework assignments. \u003c/p\u003e\n\u003cp\u003eA Cognitive-Behavioral Therapy (CBT) group model was used. CBT is one of the most rigorously researched therapeutic models and has proven effective for a wide range of issues common among prisoners, including anger, aggression, depression, anxiety, and antisocial cognitions (Lipsey, Landenberger et al. 2007). The following standardized protocol was used:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructure of the 10-Week Protocol:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 1:\u003c/strong\u003e Group Introduction \u0026amp; Building Trust. Establishing group rules, goals, and confidentiality.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 2:\u003c/strong\u003e The ABC Model. Introducing the link between thoughts and feelings/behaviors.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 3:\u003c/strong\u003e Identifying Criminal \u0026amp; Negative Thinking Patterns.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 4:\u003c/strong\u003e Cognitive Restructuring: Challenging and Changing Thoughts.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 5:\u003c/strong\u003e Anger \u0026amp; Trigger Identification.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 6:\u003c/strong\u003e Anger Management \u0026amp; Emotion Regulation Skills.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 7:\u003c/strong\u003e Problem-Solving Skills.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 8:\u003c/strong\u003e Pro-social Communication \u0026amp; Empathy Training.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 9:\u003c/strong\u003e Relapse Prevention: Applying skills to future challenges inside and outside prison.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSession 10:\u003c/strong\u003e Review, Termination, and Future Goals.\u003c/p\u003e\n\u003cp\u003eAfter completing group therapy, it can be said that participants are better able to relate to others, become aware of their own feelings and needs and express them, offer and receive support, learn new interpersonal skills they can use every day, and develop a greater awareness of themselves through sincere feedback from others. The group therapy sessions were run by the researchers. The intervention lasted for 10 weeks with 1 session weekly. \u003c/p\u003e\n\u003cp\u003eImmediately after group counseling, the questionnaire booklet was given to each participant and post-test was administered and scored by using the same tools. SPSS version 20 was used for data analysis. Percentage, paired t-test, ANOVA and simple linear regression were used to reveal the effects of counseling in boosting adjustment behavior among prisoners. At the end of the treatment, participants were thanked for their participation in the research practice and were instructed to keep themselves away from criminal activity. They were also informed that the counselors are ready to help participants in the study for any difficulty that might happen in the future. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. Research Hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study tested the following research hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.2. \u003c/strong\u003eH\u003csub\u003e1\u003c/sub\u003e: There will be statistically significant difference in the adjustment level of experimental group, which results due to the treatment effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.3. \u003c/strong\u003eH\u003csub\u003e1\u003c/sub\u003e: There will be statistically significant difference among treatment completed, no counseling and dropped out early groups on the outcome variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.4. \u003c/strong\u003eH\u003csub\u003e1\u003c/sub\u003e: Counseling has a significant positive effect on adjustment, accounting for a substantial proportion of the variability in adjustment outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7. \u003c/strong\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo be eligible for this study, participants met all of the following criteria:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Be an incarcerated adult male or female at Sodo or Tarcha Prison.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Adult prisoners (18 years or older) in Sodo or Tarcha prison.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Fluent in the language of the counseling (e.g., Amharic). \u003c/p\u003e\n\u003cp\u003e\u0026middot; Those who were held in high-security segregation and participated in a similar cognitive-behavioral program within the past year were excluded.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Capable of understanding and providing voluntary, informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8. Ethical Consideration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe goal of the study was briefly explained to each responder before the questionnaire was given out. The study was conducted with the subjects\u0026apos; consent, and the responses\u0026apos; confidentiality was maintained throughout. The involvement of the respondents was voluntary, and Wolaita Sodo University requested written consent from the Sodo and Tarcha Prison Institutions. Cultural customs and individual privacy were honored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9. Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBackground information\u0026rsquo;s and Bell Adjustment Inventory are data collecting tools used in this study. Both the Inventory and the questionnaire were prepared in Amharic language. For that matter, language experts and Psychologists were consulted to avoid language problems. Finally, the Amharic version of the questionnaire was distributed to all participants in the study and data was collected properly. These researchers assisted participants when needed or provided further explanation when requested by participants about each items in both the Inventory and the questionnaires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10. Data Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze data, the researcher used quantitative statistics. In order to accomplish this, 91 participants were selected that match the purpose of this research. Throughout the process of analysis, statistical package for social sciences (SPSS) version 20.00 was used. All significance tests were made at \u0026alpha; = 0.05. The data was visually checked up, organized and coded in a computer Excel for analysis. Then, the result was presented in the form of tables, texts, frequencies, percentage and summary statistics such as mean, standard deviation, correlation, ANOVA, simple linear regression and pared t-test to describe the study population in relation to relevant variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe major purpose of the present study was to examine the effectiveness of counseling in boosting adjustment behaviour among prisoners. This study included 38 prisoners in experimental, 41 prisoners in control and drop out groups. Totally, the intervention lasted for 10 weeks with 1 session weekly.\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSex and Age Distribution of Experimental, Control and Drop out Groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt; 22\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e23–33\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e34–44\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt; 45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDrop out\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs shown in the table, of the 38 participants in the experimental group, 27 were male and 11 were female, indicating relatively better female representation compared to the other groups. In contrast, the control group was highly male-dominated, with 38 males and only 3 females out of 41 participants. The dropout group consisted of 8 males and 4 females, suggesting a slightly more balanced distribution than the control group, although males still formed the majority. Regarding age distribution, the experimental group was predominantly younger, with 15 participants below 22 years and 21 participants between 23–33 years, while only 2 participants fell within the 34–44 age range and none were above 45. In the control group, 4 participants were below 22 years, 16 were aged 23–33, 20 were between 34–44, and 1 participant was above 45, indicating a relatively older age composition compared to the experimental group. The dropout group was mainly composed of younger individuals, particularly those below 22 years.\u003c/p\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"char\" char=\".\" colname=\"c4\" colnum=\"4\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePaired t-test of BAS Score in the Pre and Post-test in the Control Group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSodo Prison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e4.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSodo Prison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eEmotion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSodo Prison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e1.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSodo Prison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e1.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA paired-samples t-test was conducted to examine differences in BAS scores between pre-test and post-test measurements among participants in the control group at Sodo Prison. The above table indicates that there is no statistically significant mean difference between the two mean scores of Adjustment scale (Home P = 0.896; Social P = 0.307; Emotion P = 0.093; Health P = 0.114) of control groups before and after group counseling. This indicates that, in the absence of any intervention, participants’ adjustment levels remained relatively stable.\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePaired t-test of BAS score in the Pre and Post-test in the Experimental Group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eTarcha Prison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e24.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e33.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eTarcha Prison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e26.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e27.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eEmotion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eTarcha Prison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e25.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e28.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eTarcha Prison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e25.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e30.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA paired-samples t-test was conducted to assess the effect of the intervention on BAS scores among participants in the experimental group at Tarcha Prison. As shown in Table 3, there is statistically significant mean difference between the two mean scores of adjustment scale (Social P = 0.000; Emotion P = 0.000; Health P = 0.000) of experimental groups before and after group counseling. It is believed by the researchers to have been so due to the treatment effect and not by chance\u003c/p\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"left\" colname=\"c1\" colnum=\"1\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"char\" char=\".\" colname=\"c3\" colnum=\"3\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" char=\".\" colname=\"c4\" colnum=\"4\"\u003eAs shown in Table 4, a simple linear regression analysis was conducted to examine the effect of counseling on adjustment. The model summary indicates a strong positive relationship between counseling and adjustment (R = 0.814). R Square (0.663) means 66.3% of the variability in adjustment is accounted for by counseling. The ANOVA results reveal that the overall regression model is statistically significant (F (1, 77) = 151.394, p \u0026lt; 0.001). This indicates that counseling is a significant predictor of adjustment.\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"char\" char=\".\" colname=\"c2\" colnum=\"2\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" char=\".\" colname=\"c3\" colnum=\"3\"\u003eAs shown in Table 5, the one-way ANOVA results indicate a statistically significant difference among the three groups on the outcome variable, \u003cem\u003eF\u003c/em\u003e (2, 88) = 93.31, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. Specifically, participants who completed the full treatment protocol reported a substantially lower mean score (M = 9.39, SD = 4.00) compared to both the no counseling group (M = 23.93, SD = 5.84) and those who dropped out early (M = 24.50, SD = 5.13). Post hoc comparisons further revealed that these differences were statistically significant, with the completed treatment group scoring significantly lower than both the no counseling group (Mean Difference = -14.53, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and the dropout group (Mean Difference = -15.11, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). However, no significant difference was found between the no counseling and dropout groups (Mean Difference = -0.57, \u003cem\u003ep\u003c/em\u003e = .730). Overall, the findings suggest that completing the full treatment protocol was associated with significantly better adjustment (i.e., fewer imprisonments) compared with both the no-counseling and early-dropout groups.\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study shows that counselling significantly improves inmates' adjustment behaviour. The results demonstrated that, in comparison to inmates who did not receive counselling, those who did were better able to adapt emotionally, socially, and in terms of their health. The counselling and non-counseling groups showed statistically significant differences, suggesting that counselling significantly enhances inmates' capacity to adjust both inside the prison and at home after release. This is consistent with a systematic review and meta-analysis of randomized controlled trials, counseling can be effective in addressing the psychological impact of incarceration and reducing the risk of reoffending among recently released individuals. The study suggested that counseling helps a lot to development coping skills, emotional regulation, and social support, which are all important factors in promoting positive adjustment outcomes (Beaudry, Yu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother research study focused specifically on the effectiveness of counseling in promoting positive adjustment behavior found that counseling interventions were successful in helping individuals develop adaptive coping strategies, improve emotional regulation, enhance self-esteem, and foster healthier relationships. These positive adjustments were observed across various domains of life, including personal, social, and occupational functioning (Elliott, Bohart et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of psychological therapy, Cognitive-Behavioral Therapy (CBT) has been shown to significantly improve psychological adjustment among inmates, suggesting that tailored counseling approaches can effectively address the adjustment challenges faced by prisoners. The positive effects of therapy on psychological adjustment underscore counseling's vital role in helping prisoners cope with incarceration stressors and prepare for reintegration into society, as emphasized in research by (Haney \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these positive findings, it's crucial to be aware of potential drawbacks including variations in the length, nature, and demographics of counselling, which have the potential to affect the extent of progress in adjustment. To further improve the rehabilitation outcomes of inmates, future study should examine the long-term impacts of counselling and incorporate multimodal psychological therapies. Likewise, there are still many barriers to providing adequate mental health care to incarcerated individuals. These barriers include a lack of resources, stigma surrounding mental health, and a lack of trained mental health professionals (James and Glaze \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, counseling is an effective intervention for boosting adjustment behavior among prisoners, significantly contributing to positive adjustment. This underscores the need for systematic integration of counseling services in prison rehabilitation programs to foster inmates' successful adjustment and societal reintegration. Therefore, based on the findings of the present research the researchers accepted the hypothesis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFrom the findings of the study, it can be concluded that counseling service is very effective and leads to adjustment behavior once prisoners are released from prison. Furthermore, the study also revealed that counseling received prisoners who got imprisonment once had better adjustment when compared with other counterparts. Finally, it can be concluded that this finding can serve as a stepping-stone for further future studies.\u003c/p\u003e"},{"header":"Recommendations","content":"\u003cp\u003eIt is recommended therefore that:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCounseling service offered in prisons should be encouraged to suit the current needs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe community in both institutions should work together in harmony to bring long lasting change in prisoner\u0026rsquo;s life.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflicts of interest were disclosed by the author(s) with regard to the research, writing, or publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the participants who shared their experiences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson-Facile, D. (2009). Basic challenges to prisoner reentry. \u003cem\u003eSociology Compass\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 183\u0026ndash;195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaudry, G., Yu, R., Perry, A. E., \u0026amp; Fazel, S. (2021). Effectiveness of psychological interventions in prison to reduce recidivism: A systematic review and meta-analysis of randomised controlled trials. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(9), 759\u0026ndash;773.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElliott, R., Bohart, A. C., Watson, J. C., \u0026amp; Greenberg, L. S. (2011). \u003cem\u003eEmpathy Psychotherapy\u003c/em\u003e 48(1): 43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFair, H., \u0026amp; Walmsley, R. (2024). World prison population list, ICPR.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHancock, B., Ockleford, E., \u0026amp; Windridge, K. (2001). An introduction to qualitative research, Trent focus group London.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaney, C. (2001). \u003cem\u003eThe psychological impact of incarceration: Implications for post-prison adjustment\u003c/em\u003e. Urban Institute.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames, D. J., \u0026amp; Glaze, L. E. (2016). Mental health problems of prison and jail inmates.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipsey, M. W., Landenberger, N. A., \u0026amp; Wilson, S. J. (2007). Effects of cognitive-behavioral programs for criminal offenders. \u003cem\u003eCampbell systematic reviews\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 1\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha, S. (2010). Adjustment and mental health problem in prisoners. \u003cem\u003eIndustrial psychiatry journal\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(2), 101\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTravis, J. (2005). \u003cem\u003eBut they all come back: Facing the challenges of prisoner reentry\u003c/em\u003e. The Urban Insitute.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUtino, L., Birhanu, B., Getachew, N., \u0026amp; Ereso, B. M. (2023). Perceived quality of medical services at outpatient department of public hospitals in Dawro Zone, Southern Ethiopia. \u003cem\u003eBMC Health Services Research\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Voorhis, P., \u0026amp; Salisbury, E. (2016). \u003cem\u003eCorrectional counseling and rehabilitation\u003c/em\u003e. Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYesuf, Y. M., Birhan, A. A., Birara, A. G., Adimas, B. D., Bezabh, A. B., \u0026amp; Agmase, N. G. (2022). Prevalence and correlates of mental illness among inmates in North-western Ethiopia: A new look into the roles of rehabilitation service use. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 983355.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZamble, E., \u0026amp; Porporino, F. J. (2013). \u003cem\u003eCoping, behavior, and adaptation in prison inmates\u003c/em\u003e. Springer Science \u0026amp; Business Media.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 5","content":"\u003cp\u003eTable 5 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COUNSELING, ADJUSTMENT, BEHAVIOR, PRISONER, PRISON","lastPublishedDoi":"10.21203/rs.3.rs-9484453/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9484453/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the context of prison, counseling refers to a therapeutic process aimed at intervening therapeutically with individuals, primarily offenders, to address various personal, emotional, and psychological challenges. A study was conducted at Sodo and Tarcha prisons in Ethiopia to examine the effectiveness of counseling in boosting adjustment behavior among prisoners. This study employed a randomized controlled trial (RCT) with a pretest-posttest control group design. Consequently, 38 prisoners from Tarcha prison were assigned to the experimental group, while 41 prisoners from Sodo prison were assigned to the control group using the purposive sampling method. Bell's Adjustment Inventory was used to measure the adjustment level. Participants in the experimental group received group counseling, whereas the control group received no counseling at all. According to the results of the paired t-test, the adjustment level among prisons in the experimental group considerably decreased following group counseling (P\u003c0.000), whereas no change was observed in the control group. Likewise, a simple linear regression analysis indicates a strong positive relationship between counseling and adjustment (R = 0.814). R Square (0.663) means 66.3% of the variability in adjustment is accounted for by counseling. Moreover, the one-way ANOVA results indicate a statistically significant difference among treatment completed, no counseling and dropped out early groups on the outcome variable, F (2, 88) = 93.31, p \u003c .001. This study concludes that counseling is effective in enhancing adjustment behavior among prisoners.","manuscriptTitle":"The Effectiveness of Counseling in Boosting Adjustment Behaviour among Sodo and Tarcha Prisons","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 09:24:41","doi":"10.21203/rs.3.rs-9484453/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e221c91a-b063-44c9-a40a-759b573e784a","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T09:24:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 09:24:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9484453","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9484453","identity":"rs-9484453","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}