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. 2024 Sep 5;33(1):1–21. doi: 10.1080/13218719.2024.2372765

Coping, adjustment, perceived social support, and age as predictors of depressed mood among male maximum-security incarcerated offenders

Ruben Langenhoven a, Jacques Jordaan a,✉, Anni Hesselink b
PMCID: PMC12857697  PMID: 41626207

Abstract

Correctional centres tend to negatively impact the mental health of incarcerated offenders, especially those unable to cope with or adjust to the correctional environment. Studies show higher rates of psychiatric disorders like depression among offenders compared to the general population. This study aimed to determine individual and combinations of possible predictors of depressed mood amongst adult male maximum-security incarcerated offenders. This study included 418 South African male incarcerated offenders, sampled using convenience sampling, and used a quantitative, cross-sectional correlational design. The PAQ subscales (Internal Adjustment, External Adjustment, and Physical Adjustment) collectively accounted for 11.6% of the variance in the depressed mood. Hierarchical regression analysis further indicated that internal adjustment and physical adjustment were the only individual predictor variables that statistically and practically significantly predicted depressed mood. This study’s findings could aid in understanding the role of adjustment in developing and maintaining depressive mood states in the correctional environment.

Keywords: adjustment, coping, depression, male offenders, perceived social support, predictor variables, private maximum-security correctional centre, South Africa

Background

South Africa is internationally known for high rates of violent and aggressive offences (Jordaan & Hesselink, 2022; Pretorius et al., 2022; Thobane & Prinsloo, 2018), with convicted offenders sentenced to one of the 243 correctional centres in South Africa (Department of Correctional Services [DCS], 2022a, 2022b). On 31 March 2022, the total number of incarcerated offenders in South Africa was 143,223, against the official bed space of 108,804, resulting in a 131.63% occupancy level (DCS, 2022b). These overcrowded correctional centres are further known for the high prevalence of gang activity, violence, sexual victimisation, exploitation, murder, and suicide (Gear, 2007; Jordaan & Hesselink, 2022; Pretorius et al., 2022). Consequently, correctional centres often negatively impact the mental health of incarcerated offenders, especially when they cannot cope with or adjust to the correctional environment (Pretorius et al., 2022; Rogers et al., 2022). Incarcerated offenders who are unable to cope or adjust to the correctional environment face various psychological challenges, including depression, anxiety, emotional withdrawal, substance abuse, feelings of worthlessness, paranoia, hostility, and suicidal ideation or attempts (Casey et al., 2016; DeVeaux, 2013; Steiner et al., 2014). Internationally, studies have found significantly higher rates of psychiatric disorders such as depression among incarcerated offenders compared to the general population (Andreoli et al., 2014; Baranyi et al., 2019; Reingle Gonzalez & Connell, 2014). The disproportionately high rate of psychiatric disorders among offenders has been conceptualised as both a precipitant and a consequence of their offender status (Lamberti, 2016; Polanin et al., 2021; Schucan Bird & Shemilt, 2019). Several studies have highlighted the effects of incarceration on post-release mental health and how mental health factors influence the process of reintegration into society (Chang et al., 2015; Mowen et al., 2020; Schroeder et al., 2022; Shinkfield & Graffam, 2010). Shishane et al. (2023) found that the presence of psychiatric disorders predicted higher recidivism rates among South African offenders. Therefore, variables predicting depressed mood are significant to research, given the high prevalence and detrimental impact of depression on offenders during incarceration and post-release.

Incarceration experience

Offenders often describe incarceration as traumatic (DeVeaux, 2013; Muntingh, 2009; Picken, 2012). The loss of freedom, separation from loved ones, and difficulties adapting to a rigid and punitive environment negatively impact the mental health of incarcerated offenders (Jordaan, 2014). This is further compounded by the relatively high incidence of adverse life events (e.g. poverty, violence, and substance abuse) and pre-existing psychiatric disorders (e.g. personality disorders, trauma, and stressor-related disorders, depressive disorders, and anxiety disorders) that characterise the offender population (Bowen et al., 2018; Fazel et al., 2016; Liu et al., 2021). According to the importation model by Irwin and Cressey (1962), offenders’ personal traits (e.g. impulsivity) and social background (e.g. familial dysfunction and criminal peers) influence their adjustment to, and behaviour in, the correctional environment (DeLisi, 2003; Toman et al., 2015). Pre-correctional adversities such as abuse, unemployment, poverty, and substance abuse (Shong et al., 2019) predispose incarcerated offenders to develop psychiatric disorders (Armour, 2012; Wilton & Stewart, 2017). Additionally, therefore, the incarceration experience is mediated by offenders’ social background and the individual criminogenic characteristics that are imported and superimposed on one another in the correctional environment (Jordaan & Hesselink, 2018; Lai, 2019). Parallel to the importation model, Sykes (1958) proposed the deprivation model, which conceptualises the high incidences of mental and physiological illness among offenders as perpetuated by environmental factors, including overpopulation, lacking privacy, violence, and lack of access to health services (Hesselink & Booyens, 2021; Simpson & Butler, 2020). The deprivation model views the correctional centre as a system of social, psychological, and physical deprivation, which further aggravates the loss of freedom (Crewe, 2021; Haney et al., 2016; Woo et al., 2016). Therefore, deprivation is a central theme of the incarceration experience, as incarcerated offenders’ privacy, autonomy, social interactions, and access to resources are highly restricted, which may precipitate and perpetuate mental health difficulties.

Gang-related activity, which is linked to both importation and deprivation models, is also pervasive in the South African correctional environment and may involve bullying, sexual victimisation, exploitation, violence, and murder (Gear, 2007; Jordaan & Hesselink, 2022; Pretorius et al., 2022; Rogers et al., 2022). Consequently, offenders may resort to violence and gang activity to protect themselves (Ricciardelli, 2014) or increase their status (Crewe et al., 2020; Ricciardelli, 2014). Although prison gangs often perpetuate violence in correctional centres, they, paradoxically, provide members with a sense of structure, safety, and status (Lindegaard & Gear, 2014), making gang affiliation particularly attractive to incarcerated offenders, with the infamous Number gangs (the 26s, 27s, 28s, Big5, and Airforce 3 and 4 prison gangs) dominating the South African correctional environment (Grobler & Hesselink, 2015; Skywalker, 2014).

According to literature, the incarceration experience can and does lead to the experience of depression symptomatology within the correctional environment (Beyen et al., 2017; Porter & Novisky, 2017; Turney et al., 2012). In this study, coping, adjustment, perceived social support, and age were investigated as possible predictors of depressed mood among South African incarcerated offenders (Casey et al., 2016; Chahal et al., 2016; Richie et al., 2021; Rogers et al., 2022; Ukeh & Hassan, 2018). Specifically, this study aimed to determine which predictor variable(s), or set of predictor variables, explained a significant percentage of variance in Depressed Mood among adult male maximum-security incarcerated offenders in a private South African maximum-security correctional centre.

Depressed mood in the correctional environment

The DSM-V-TR (Diagnostic and Statistical Manual of Mental Disorders, Text Revision) defines a major depressive episode as characterised by low mood and/or the loss of interest or pleasure in activities for a period of at least two weeks (American Psychiatric Association [APA], 2022). Associated cognitive and somatic changes may include weight changes, sleep disturbances, fatigue or loss of energy, diminished ability to concentrate, psychomotor agitation, and suicidal ideation (APA, 2022; Beck et al., 1996). Bedaso et al. (2020) estimated the global prevalence of depression among the offender population in developing countries at 39.2%. Previous South African studies found lifetime prevalence rates of 27.5% (Prinsloo, 2013) and 24.9% (Naidoo & Mkize, 2012) among respective offender samples. In a study by Modupi et al. (2020), 8% of offenders admitted to health facilities were diagnosed with major depressive disorders. Additionally, suicide, which has a well-documented association with depression (Orsolini et al., 2020; Ponsoni et al., 2018), remains the primary cause of unnatural death among South African incarcerated offenders (DCS, 2022b; Rawoot, 2012). In 2015, 61 suicides occurred in correctional centres in South Africa, yielding a prevalence rate of 36:100,000, almost three times the national suicide prevalence rate (Bantjes et al., 2017). Most recently, in the 2021/2022 financial year, 26 offenders committed suicide, accounting for 50% of unnatural deaths in the South African correctional environment (DCS, 2022b).

Coping and depression

Coping involves individuals’ cognitive and behavioural strategies to manage their environmental stressors (Compas et al., 2001). Coping has a well-established, linear relationship with mental health, where better coping is associated with improved mental health (Carr, 2013; Chahal et al., 2016; Taylor & Stanton, 2007; Ukeh & Hassan, 2018). Offenders who rely on adaptive coping strategies, such as problem-solving and seeking social support, are less likely to resort to aggression and tend to experience incarceration more positively (Bouffard, 2015; Jordaan, 2014). However, offenders frequently exhibit maladaptive coping styles, such as aggression, violence, and avoidance (Jordaan & Hesselink, 2022; McKeown et al., 2017; Rocheleau, 2013), which are associated with decreased well-being (Asberg & Renk, 2014; Rose et al., 2020), maladjustment (Rogers, 2019; Rogers et al., 2022), and an increased risk of psychiatric disorders including depression and anxiety (Hayat & Zafar, 2015; Picken, 2012).

Adjustment and depression

Adjustment involves the process of successfully adjusting to the challenges and deprivations of the correctional environment (Picken, 2012). Wright (1985) conceptualised adjustment to incarceration as the offender’s ability to procure environmental necessities including food, privacy, sleep, and social ties. Well-adjusted offenders are less likely to commit institutional misconduct (Dye, 2010; Gonçalves et al., 2014) and have less difficulty reintegrating into society post-release (Canda et al., 2015). However, the unique combination of challenges posed by the correctional environment often encumbers adjustment (Chen et al., 2014; Van Ginneken, 2015). Consequently, maladjustment occurs when offenders are unable to meet environmental and/or internal demands (Wright, 1991). Maladjusted offenders are more likely to experience depression, anxiety, and suicidal ideation (Casey et al., 2016; Drury & DeLisi, 2010; Dye, 2010; Rogers, 2019; Rogers et al., 2022).

Perceived social support and depression

Perceived social support involves offenders’ perception of emotional help and the support they receive from friends, family, and significant others (Liu & Chui, 2014; Zimet et al., 1990). Social support has been linked to better adjustment to the correctional environment, prosocial behaviour, and greater life satisfaction (Chen et al., 2014; Onyishi et al., 2012). Additionally, perceived social support has been linked with lower rates of suicidal ideation among offenders (Richie et al., 2021). As a form of social support, visitation significantly reduces the probability of misconduct, victimisation, and future crime engagement (Meyers et al., 2017; Woo et al., 2016). Social support from fellow offenders has also been shown to reduce institutional misconduct and lessen offenders’ engagement in risky behaviours (Spohr et al., 2016). Importantly, social support has been highlighted as a fundamental factor in the process of desistance (Chouhy et al., 2020), as social support produces a feeling of reciprocity which motivates change in offenders (Cid & Martí, 2017; Fox, 2022).

Age and depression

Unver et al. (2013) found no statistically significant differences in depression and anxiety scores for incarcerated offenders aged 18–69. While it has been suggested that older offenders are at an increased risk of falling victim to bullying and intimidation (Codd, 2020; Kerbs & Jolley, 2007), some studies have also posited that, through experience, older offenders develop the necessary strategies to cope with the demands of the correctional environment (Baidawi, 2016; Butler, 2019; Casey et al., 2016), reducing the adverse effects of incarceration on mental health. Younger offenders, on the other hand, tend to experience greater difficulty adjusting to the correctional environment, often resulting in increased aggression and institutional misconduct (Jordaan & Hesselink, 2022; Valentine et al., 2015). However, there are limited studies regarding the mental health of older offenders (Haesen et al., 2019; Stoliker & Galli, 2019), hampering the availability of comparative data on depressed mood across offender ages.

Purpose of this study

The high prevalence of mental health concerns among South African incarcerated offenders is worrying, especially considering that offenders are at greater risk of experiencing anxiety, paranoia, feelings of worthlessness, suicidal thoughts or attempts, as well as hostility towards others (Casey et al., 2016; DeVeaux, 2013; Kalin, 2020; Steiner et al., 2014). This study aimed to determine which predictor variable(s), or set of predictor variables, explained a significant percentage of variance in Depressed Mood among adult male maximum-security incarcerated offenders in a private South African maximum-security correctional centre. It is hypothesised that each of the predictor variables, as well as the different combinations of the predictor variables, will explain a significant percentage of variance in depressed mood, as these predictor variables have been linked to depression in literature.

Method

Research design

This study followed a quantitative, non-experimental approach using a cross-sectional correlational research design (Bloomfield & Fisher, 2019; Curtis et al., 2016; Stangor, 2015).

Participants and sampling

This study involved analysis of data originally collected by Rogers (2019). Thus, secondary data analysis (Johnston, 2014; Tripathy, 2013) was conducted in this study to answer a different question from the original research project. In the original study, 418 incarcerated male maximum-security offenders between the ages of 21 and 58 were sampled using non-probability convenience sampling (Stangor, 2015). Recruitment to the study was made available via the correctional centres’ schooling programme, as well as the therapeutic programme hosted by psychologists and social workers. The only exclusion criterion applied in that study was an inability to read, write, or understand English. There were no incentives associated with participation in the study. The mean age of the study participants was 33.73 years (SD = 6.42). Respondents’ self-identified ethnicity was 82.8% African, 13.9% Coloured (Mixed race), 2.6% White, and 0.7% Asian or ‘Other’. Of the study participants, 227 (54.3%) were first-time offenders, while the remaining 191 (45.7%) had been incarcerated more than once. Regarding sentence length, 97.1% of the sample group was sentenced for 15 or more years and 43.5% of the offenders sentenced for 25 or more years. At the time of the study, 97.4% of the offenders had served less than 10 years of their sentences. A total of 43.3% of offenders indicated an affiliation with a gang when asked about gang involvement in the correctional environment.

Measurement scales

A demographic questionnaire, in conjunction with four measurement scales was used to collect the data. The demographic questionnaire included offender demographics such as age, ethnicity, education level, nature of the offence, and length of sentence.

The Beck Depression Inventory-II (BDI-II; Beck et al., 1996) was used to measure self-reported depression among offenders. The BDI-II consists of 21 items formulated according to the DSM-IV criteria for major depression (Wang & Gorenstein, 2013). The diagnostic criteria include depressed mood, anhedonia, sleep disturbances, weight changes, psychomotor agitation, fatigue or loss of energy, diminished ability to concentrate, and suicidal ideation (APA, 2022). For each item, respondents must indicate the severity of a particular depression symptom, with response options ranging from 0 (“the symptom is absent”) to 3 (“the symptom is pervasive”) (Beck et al., 1996). A higher total score is indicative of more severe depressive symptomatology. For this study, depressive mood included subclinical and clinical levels of symptomology. Various studies found good to exceptional internal consistencies for the BDI-II, ranging from .84 to .94 (Park et al., 2019;; Sacco et al., 2016; Wang & Gorenstein, 2013). In this study, the BDI-II yielded a good internal consistency of .86.

The Prison Adjustment Questionnaire (PAQ; Wright, 1985) was used to measure the participants’ adjustment to the correctional environment. The PAQ consists of 30 items and three subscales: (a) Internal Adjustment, (b) External Adjustment, and (c) Physical Adjustment (Wright, 1985). Higher scores on the PAQ suggest difficulties adjusting to the correctional environment (Wright, 1985). This study yielded adequate internal consistencies of .73 for Internal Adjustment, .76 for External Adjustment, .71 for Physical Adjustment, and .78 for the Global Score of the PAQ. These results are consistent with previous South African studies, where adequate to exceptional internal consistencies for the subscales, ranging from .68 to .72 for Internal Adjustment, .76 to .92 for External Adjustment, and .70 to .71 for Physical Adjustment, were found (Duba & Jordaan, 2023; Rogers, 2019; Rogers et al., 2022).

The Coping Strategy Indicator (CSI; Amirkhan, 1990) was used to measure the participants’ coping strategies. The CSI consists of 33 items and three subscales: (a) Problem-solving, (b) Seeking Social Support, and (c) Avoidance. Higher scores on each subscale indicate a greater likelihood of using the associated coping strategy. In this study, adequate to good internal consistencies were found, namely .80 for Problem-solving, .85 for Seeking Social Support, .63 for Avoidance, and .78 for the Global Score on the CSI. This is aligned with previous studies in the South African context where adequate to exceptional internal consistencies ranging from .82 to .98 for Problem-solving, .88 to .98 for Seeking Social Support, and .75 to .96 for Avoidance, were reported (Jordaan, 2014; Jordaan & Hesselink, 2022; Pretorius et al., 2022; Rogers et al., 2022).

The Multidimensional Scale of Perceived Social Support (MSPSS; Zimet et al., 1990) was used to measure the levels of perceived social support of the participants. The MSPSS consists of 12 items measuring perceived social support from three distinct sources, namely (a) Friends, (b) Family, and (c) Significant Others (Zimet et al., 1990). Higher scores on each subscale suggest greater perceived social support from the associated group. Good to exceptional internal consistencies, ranging from .84 to .92 for the total score, have been reported in previous studies (Brown & Day, 2008; Zimet et al., 1990). In this study, the MSPSS yielded adequate to good internal consistencies, namely .86 for Family, .78 for Friends, .84 for Significant Others, and .91 for the Global Score on the MSPSS. Previous South African studies also yielded good to exceptional internal consistencies, ranging from .89 to .90 for the Friends subscale, .90 to .93 for the Family subscale, and .89 to .91 for the Significant Others subscale (Duba & Jordaan, 2023; Rogers, 2019; Rogers et al., 2022).

Procedure

The authors obtained ethical clearance and official permission to conduct this study from ethics committee of the university, with which they are affiliated, and from the Department of Correctional Services. As this study involved secondary analysis of existing data, written permission was obtained from the research team who gathered the raw data. Given the vulnerable nature of the population, secondary data analyses posed numerous advantages, as this reduced the risk of harm to participants, facilitating a positive offset in the risk–benefit ratio (Mauthner & Parry, 2013).

Data analysis

The data were analysed using the Statistical Package for the Social Sciences (SPSS) (IBM, 2023). In order to assess distributional issues and the relationships between variables, descriptive statistics (mean, standard deviation, skewness, and kurtosis) and Pearson correlations were calculated. Thereafter, hierarchical multiple regression analysis (Stangor, 2015) was conducted, investigating the percentage of variance in depression of male maximum-security incarcerated offenders, explained by the different predictor variables, namely Age, Coping (i.e. Problem-solving, Seeking Social Support, Avoidance), Adjustment (i.e. Internal Adjustment, External Adjustment, Physical Adjustment), and Perceived Social Support (i.e. Family, Friends, Significant Others). The percentage of variance explained by each individual predictor variable was investigated as well as the percentage of variance explained by the following combinations of predictor variables, namely: (a) all the predictor variables; (b) Age; (c) Problem-solving, Seeking Social Support, and Avoidance as coping; (d) Internal Adjustment, External Adjustment, and Physical Adjustment as adjustment; and (e) Friends, Family, and Significant Others as Perceived Social Support. Effect sizes were calculated to determine the practical significance of the findings (Steyn, 2009). For correlations, Steyn (2009) stated that an effect size of .10 is considered small, an effect size of .30 is medium, and an effect size of .50 is considered large. According to Cohen (1992), when performing hierarchical regression analyses, an effect size of .02 is considered small, an effect size of .15 is considered medium, and an effect size of .35 is considered large. Both the 1% and 5% levels of significance were used in the data analyses. Results examined and reported on in this study include only those that tended towards a medium effect size and were statistically significant.

Results

Pearson correlations

Table 1 illustrates the Pearson correlations that were calculated between the predictor (independent) variables and the dependent (outcome) variable. All the assumptions of correlational analyses (i.e. normality, linearity, homoscedasticity, outliers) were met.

Table 1.

Correlations between the BDI-II and age, the Global Scores and subscales of the CSI, PAQ, and MSPSS.

  Depressed mood p
CSI .363** ≤.001
 Problem-solving .259** .000
 Seeking Social Support .261** .000
 Avoidance −.156** .001
PAQ −.373** ≤.001
 Internal Adjustment −.408** .000
 External Adjustment −.181** .000
 Physical Adjustment −.350** .000
MSPSS −.276** ≤.001
 Family −.240** .000
 Friends −.252** .000
 Significant Others −.226** .000
Age .003 .950
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Note: N = 418.

**p ≤ .01.

Hierarchical regression analyses

Hierarchical regression analysis was conducted to examine the contribution of the different combinations of predictor variables (coping, adjustment, perceived social support, and age) to the percentage of variance in depressed mood1 as well as the contribution of each of the individual predictor variables. All the assumptions of regression analyses (i.e. normality, multi-collinearity, and normality, linearity, and homoscedasticity of residuals) were investigated, and none of the assumptions was violated.

From Table 2, it is evident that the combination of the predictor variables accounts for 28.9%, F(10, 407) = 16.531, p ≤ .001, of the variance in the Depressed Mood scores of the sample. The corresponding large effect size (f 2 = .41) suggests that this finding is of practical significance. Additionally, the PAQ subscales (Internal Adjustment, External Adjustment, and Physical Adjustment), as a set (combination) of predictor variables, account for 11.6% of the variance in the Depressed Mood scores of the offenders. This finding is statistically significant at the 1% level, and the corresponding medium effect size (f 2 = .16) suggests that this finding is of practical significance.

Table 2.

Contributions of age, the CSI subscales, the PAQ subscales, and the MSPSS subscales to R2 with Depressed mood as criterion variable.

Variables in equation R 2 Contribution to R2 F f 2
1. [Age] + [PS + SSS + AV] + [IA + EA + PA]   + [FAM + FRI + SO] .289 1–5 = .022 4.197843** .03
2. [Age] + [PS + SSS + AV] + [IA + EA + PA] + FAM .288 2–5 = .021 12.0632** .03
3. [Age] + [PS + SSS + AV] + [IA + EA + PA] + FRI .276 3–5 = .009 5.084254* .01
4. [Age] + [PS + SSS + AV] + [IA + EA + PA] + SO .281 4–5 = .014 7.963839** .02
5. [Age] + [PS + SSS + AV] + [IA + EA + PA] .267      
6. [Age] + [PS + SSS + AV] + [FAM + FRI + SO]   + [IA + EA + PA] .289 6–10 = .116 22.13408** .16
7. [Age] + [PS + SSS + AV] + [FAM + FRI + SO] + IA .260 7–10 = .087 48.08514** .12
8. [Age] + [PS + SSS + AV] + [FAM + FRI + SO] + EA .176 8–10 = .003 1.489078 –
9. [Age] + [PS + SSS + AV] + [FAM + FRI + SO] + PA .236 9–10 = .063 33.72644** .08
10. [Age] + [PS + SSS + AV] + [FAM + FRI + SO] .173      
11. [Age] + [IA + EA + PA] + [FAM + FRI + SO]   + [PS + SSS + AV] .289 11–15 = .056 10.68542** .08
12. [Age] + [IA + EA + PA] + [FAM + FRI + SO] + PS .266 12–15 = .033 18.38828** .05
13. [Age] + [IA + EA + PA] + [FAM + FRI + SO] + SSS .261 13–15 = .028 15.49662** .04
14. [Age] + [IA + EA + PA] + [FAM + FRI + SO] + AV .237 14–15 = .004 2.144168 .01
15. [Age] + [IA + EA + PA] + [FAM + FRI + SO] .233      
16. [PS + SSS + AV] + [IA + EA + PA]   + [FAM + FRI + SO] + [Age] — 16–17 = .000 — —
17. [PS + SSS + AV] + [IA + EA + PA]   + [FAM + FRI + SO] —      
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Note: PS = Problem-solving, SSS = Seeking Social Support, AV = Avoidance, IA = Internal Adjustment, EA = External Adjustment, PA = Physical Adjustment, FAM = Family, FRI = Friends, SO = Significant others. Contribution to R2: full minus reduced model.

*p ≤ .05; **p ≤ .01.

Additional multiple hierarchical regression analyses were conducted with the Global Scores of the CSI, PAQ, and MSPSS as the predictor variables, with Depressed Mood as the dependent (criterion) variable (Table 3).

Table 3.

Contributions of the Global Scores of the CSI, PAQ, MSPSS to R2 with Depressed Mood as the criterion variable.

Model R2 R2 change F df 1 df 2 p
1a .139 .139 67.270** 1 416 <.001
2b .212 .072 38.163** 1 415 <.001
3c .231 .019 10.476** 1 414 .001
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aModel 1: Predictors: (Constant), Adjustment. bModel 2: Predictors: (Constant), Adjustment, Coping. cModel 3: Predictors: (Constant), Adjustment, Coping, Perceived Social Support.

**p < .01.

In the first step, adjustment, as measured by the PAQ, was entered (Model 1). Adjustment was entered in the first step as the findings of the regression analyses in Table 2 indicated that Adjustment is a statistically and practically significant predictor of Depressed Mood. In the second and third steps (Models 2 and 3), Coping and Perceived Social Support, as measured by the CSI and MSPSS, respectively, were added, in addition to Adjustment. The results showed that Model 1 was statistically significant at the 1% level, with F(1, 416) = 67.270, p ≤ .001, R2 = .139, adjusted R2 = .137. Scores on the PAQ were statistically significantly associated with Depressed Mood (b = −.373, t = −8.202, p ≤ .001). Furthermore, Model 2 was statistically significant at the 1% level, with F(1, 415) = 38.163, p ≤ .001, R2 = .212, adjusted R2 = .208, and showed an improvement from Model 1, with ΔF(2, 415) = 55.721, p ≤ .001, ΔR2 = .072. In this model, the scores on the CSI were statistically significantly associated with Depressed Mood (b = .281, t = 6.178, p ≤ .001). Lastly, Model 3 was statistically significant at the 1% level, with F(1, 414) = 10.476, p = .001, R2 = .231, adjusted R2 = .226, and showed an improvement on Model 2, with ΔF(3, 414) = 41.488, p ≤ .001, ΔR2 = .019. In this model, the scores on the MSPSS were statistically significantly associated with Depressed Mood (b = −.147, t = −3.237, p = .001). Overall, adjustment accounted for 13.9% of the variance in Depressed Mood, while Coping and Perceived Social Support accounted for 7.2% and 1.9%, respectively, of the variance in the Depressed Mood scores of the offenders.

Discussion

Gilbert (2016) provides various conceptualisations of the aetiology of depression, including depression due to lacking social power, depression resulting from thwarted needs, and depression borne in muffled aspirations and the resulting hopelessness. These aetiological descriptions are certainly evident within the correctional environment and echo the postulations of the importation (Irwin & Cressey, 1962) and deprivation (Sykes, 1958) models. Correctional centres are inherently pathological, and mental health depends on the individual’s ability to tolerate deprivation within the correctional environment. However, most offenders come from violent and socio-economically disadvantaged backgrounds, adversely affecting factors such as coping, adjustment, and social support, thus rendering them highly vulnerable and susceptible to the development of psychiatric disorders (e.g. depression) (Armour, 2012; Baglivio et al., 2015; Beckley et al., 2018; De Viggiani, 2007; Levenson et al., 2016). This is concerning, as poorer mental health has also been linked to an increased risk of recidivism (Shishane et al., 2023; Wallace & Wang, 2020)., It is, therefore, important to understand how factors such as coping, adjustment, perceived social support, and age act as predictors of Depressed Mood.

Pearson correlation findings

Adjustment and depressed mood

The Internal Adjustment Subscale of the PAQ correlated indirectly with Depressed Mood, significant at the 1% level, with a medium effect size (.41; CI = 99%). This finding seems to suggest that as male maximum-security incarcerated offenders report better internal adjustment to the correctional environment, they also report an increasingly depressed mood. Conversely, this finding might imply that when incarcerated offenders experience less depressive symptomatology, their internal adjustment to the correctional environment worsens. Table 1 further indicates an indirect correlation between the Physical Adjustment subscale of the PAQ and Depressed Mood, significant at the 1% level, with a medium effect size (.35; CI = 99%). This finding suggests that as male maximum-security incarcerated offenders’ physical adjustment improves, they experience an increasingly depressed mood. This finding may also suggest that as male maximum-security incarcerated offenders’ depressed mood decreases, they are more likely to experience poorer physical adjustment. Furthermore, there is a statistically significant negative correlation between the PAQ Global Score and Depressed Mood, with a medium effect size (.37; CI = 99%). This finding seems to suggest that as male maximum-security incarcerated offenders’ adjustment improves, they experience an increasingly depressed mood. This finding may also suggest that as male maximum-security incarcerated offenders’ depressed mood decreases, they are more likely to experience poorer adjustment to the correctional environment. These findings are incongruous with the existing literature, which suggests that better adjustment is associated with decreased depressed mood (Duba & Jordaan, 2023; Dye, 2010; Picken, 2012; Rogers et al., 2022).

Coping and depressed mood

Additionally, the CSI’s Seeking Social Support and Problem-solving subscales, as well as the CSI Global Score, positively correlated with Depressed Mood. The correlations with the CSI subscales were significant at the 1% level, with inclinations towards medium effect sizes, while the correlation with the CSI Global Score had a medium effect size (.36). These findings may suggest that as the male offenders’ seeking social support, problem-solving, and coping increases, their depressed mood seems to increase. Alternatively, these findings may also suggest that as the male offenders’ depressed mood decreases, they are less likely to seek social support or use problem-solving as coping strategies. Although previous studies have yielded mixed findings, the findings from this study seem to support a growing body of literature regarding the potentially negative relationship between seeking social support as a coping mechanism and mental health (Gear & Ngubeni, 2002; Kjellstrand et al., 2022; Pettus-Davis et al., 2018; Rogers et al., 2022). Gang involvement and associated risky behaviours (e.g. violence and smuggling of contraband) are examples of how seeking social support may be linked to poorer mental health (Gear, 2007; Rogers et al., 2022). Similarly, previous studies found problem-solving ineffective and potentially frustrating (Chahal et al., 2016; Picken, 2012), as offenders have limited control over problems in the correctional environment.

Seeking social support and depressed mood

Finally, all the subscales of the MSPSS (Friends, Family, and Significant Others), as well as the MSPSS Global Score, yielded negative, indirect correlations with Depressed Mood. All these correlations were statistically significant, but none of them was practically significant. These findings may suggest that as male offenders’ perceived social support from others increases, their depressed mood seems to decrease. Thus, the findings confirm previous studies, where receiving social support through friendships, family members, and significant others was associated with improved mental health (Duba & Jordaan, 2023; Rogers et al., 2022).

Hierarchical regression analysis

The findings of the hierarchical regression analyses indicated that the combination of all predictor variables significantly predicted depressed mood. This implies that coping, adjustment, perceived social support, and age, in combination, are relevant factors in predicting depressed mood within the correctional environment. In terms of individual predictors, adjustment, as measured by the combination of the PAQ subscales, was the only predictor that was both statistically and practically significant in predicting depression mood. The combined subscales of the MSPSS, as well as the combined subscales of the CSI, were statistically, but not practically significant in predicting depressed mood. Finally, age was not statistically predictive of depressed mood.

The combination of the PAQ subscales significantly predicted depressed mood, accounting for 11.6% of the variance in Depressed Mood. Interestingly, better adjustment to the correctional environment was predictive of an increasingly depressed mood. These results contradict previous findings asserting that as adjustment to the correctional environment improves, offenders experience a decrease in depressed mood and associated symptoms, such as withdrawal and suicidal ideation (Duba & Jordaan, 2023; Dye, 2010; Picken, 2012; Rogers et al., 2022). Thus, the current study’s findings suggest that adjustment to the correctional environment does not always protect against depressed mood. In addition to the PAQ subscales as a combination predicting depressed mood, this study also found statistically and practically significant indirect correlations between Internal Adjustment and Depressed Mood and Physical Adjustment and Depressed Mood. It is noteworthy that External adjustment also yielded a statistically significant indirect correlation with Depressed Mood, although the effect size was small (.18; CI = 99%).

Towards making sense of the seemingly contradictory results in this study, the subscales of the PAQ were examined more closely. Internal Adjustment relates specifically to (a) discomfort around other incarcerated offenders and correctional centre staff, (b) anger and (c) trouble sleeping (Wright, 1985). In terms of the discomfort around others and the experience of anger, existing research found a direct relationship between depression and social withdrawal (Girard et al., 2014; Ladd et al., 2021; Porcelli et al., 2019). Therefore, it was hypothesised that incarcerated offenders who are depressed might exhibit social withdrawal, possibly leading to reduced interactions and fewer opportunities for experiencing discomfort and/or anger towards others. The inverse may also be true, where incarcerated offenders who are socially engaged are at an increased risk for adverse social experiences (e.g. rejection, intimidation, and deviant peers), leading to interpersonal discomfort and/or anger (Biglan et al., 2017; Simons & Burt, 2011). Alternatively, emotional blunting, a common symptoms of major depressive disorder (Christensen et al., 2021; Goodwin et al., 2017), may account for reduced experiences of discomfort and/or anger in the correctional environment. Regarding trouble sleeping, a large-scale meta-analysis reported a U-shaped relationship between sleep duration and depression, suggesting that both short and long sleep duration was predictive of depression (Zhai et al., 2015). Psychiatric disorders, particularly depression, are frequently associated with excessive daytime sleepiness and hypersomnia (Dauvilliers et al., 2013; Kaplan & Harvey, 2009). However, questions in the PAQ regarding sleep quality focus exclusively on difficulty sleeping (e.g. insomnia), potentially skewing the relationship with depressed mood.

Physical Adjustment relates to: (a) fear of attack, (b) perceived risk of injury and instances of injury, (c) frequency of illness, and (d) instances of being taken advantage of by other incarcerated offenders. Regarding the fear of being attacked, depression has a well-established relationship with social withdrawal (Girard et al., 2014; Ladd et al., 2021; Porcelli et al., 2019), which may reduce instances where there is an actual or perceived threat of being attacked in the correctional environment. This argument could also apply to the instances of being taken advantage of, as offenders who avoid social interactions may be at reduced risk of being taken advantage of. Regarding risk and instances of injury, behavioural deficits directly correlate with depression (Roshanaei-Moghaddam et al., 2009). Therefore, the risk and instances of injury may be reduced in depressed offenders, as depression is associated with sedentary behaviour (Patten et al., 2009). In terms of physical illness, the assumption of the sick role could be a viable explanation for the apparent indirect correlation between illness and depression, as incarcerated offenders may receive additional care and benefits while sick (Forry et al., 2019), potentially reducing symptoms of depressed mood.

Regarding the indirect correlations between the three subscales of the PAQ and BDI-II, it was also important to consider the correctional environment as a unique milieu, internally divergent, as well as highly distinct from the free world (Jordaan, 2014; Matshaba, 2007; Pretorius et al., 2022). Consequently, conventional conceptualisations of depressed mood, as measured by the BDI-II, may not readily apply within the correctional environment. As such, the relationship between adjustment and depressed mood in the correctional environment may be more complex than previously thought. Additionally, it is proposed that, as with all self-report questionnaires, the respondents’ subjective interpretation of the questions of the PAQ and BDI-II could have potentially distorted findings unilaterally. Many questions on the PAQ deal with discomfort around other offenders, fear of other offenders, arguments with other offenders and staff, and being taken advantage of by other offenders (Wright, 1985). The nature of these items is an important consideration in the correctional environment, where perceived masculinity and toughness often determine status and hierarchy (Carlsson, 2013; Maguire, 2021). Therefore, respondents may have been motivated to misrepresent themselves on the self-report questionnaires.

The findings of the hierarchical regression analyses using the Global Scores confirmed that Adjustment made the strongest unique contribution to the explanation of the variance in the Depressed Mood scores of the sample, followed by Coping and Perceived Social Support. From a learned helplessness perspective, Haney (2001) argued that offenders who tend to adjust well to the strict routines and regulations of the correctional environment tend to develop a sense of learned helplessness. This sense of learned helplessness might occur when incarcerated offenders feel as if they have no or little control over their environment, leading to feelings of hopelessness and depression. Zamble and Porporino (1988) also discussed learned helplessness as a significant outcome of lengthy sentences, where offenders develop a sense of futility and resignation due to the lack of control over their environment. Thus, the more offenders conform to the norms and routines within the correctional environment, the more they may internalize a sense of powerlessness and inevitability about their situation, contributing to depressive symptoms (Haney, 2001). Additionally, coping strategies that offenders tend to develop to cope with the correctional environment, might often require suppressing emotions and becoming compliant with the rules and regulations of the correctional environment. While these strategies tend to reduce immediate stress and conflict, they can lead to long-term mental health concerns such as depression due to the loss of identify and autonomy (Haney, 2001).

The aim of this study was to determine which variables, or sets of variables, serve as the best predictors of depressed mood among male maximum-security incarcerated offenders. Previous research has shown that the presence of depression significantly increases offenders’ likelihood of engaging in suicidal behaviours, violence, and substance abuse (Gonçalves et al., 2014; Leigh-Hunt & Perry, 2015), as well as increasing the risk of recidivism (Wallace & Wang, 2020). As such, it is imperative to better understand the predictors of depression in correctional contexts to facilitate the development of specialised diagnostic and therapeutic interventions (Fazel et al., 2016; Franke et al., 2019).

Limitations

A limitation of this study was the uniqueness of the data collection site, a private maximum-security correctional centre at the time of data collection (Rogers, 2019), and the homogeneity of the participants. Consequently, the results of this study are not generalisable to incarcerated offenders housed in public correctional centres in South Africa and beyond. Another limitation pertaining to data collection was the use of convenience sampling as well as English literacy as the inclusion criterion, introducing a margin of selection bias (Stangor, 2015). The data used to formulate this study’s results were also gathered using self-report measures. Self-report measures constitute a limitation, as respondents may be motivated to misrepresent themselves for various reasons, such as the offenders portraying themselves in a favourable light in the hope that it will improve parole prospects (Seager, 2005).

Recommendations

Future research can endeavour to further investigate the relationship between adjustment and depression in South African correctional centres. Similar research can be conducted at government-operated minimum-, medium-, and maximum-security correctional centres and with a more varied sample of offenders to ensure the generalisability of the findings.

Furthermore, numerous studies (Liem & Kunst, 2013; Porter, 2019; Porter & DeMarco, 2019; Schnittker, 2014; Sugie & Turney, 2017) have demonstrated the short, medium and long-term detrimental effects of incarceration on mental health. As such, it is recommended that future research investigate the post-release predictors of the mental health of previously incarcerated offenders.

Value of this study

The Research Agenda of the Department of Correctional Services (2019) indicated that offender research is crucial for developing and refining policies and procedures. This study contributed to the South African body of knowledge on offender populations and, more specifically, the mental health of incarcerated offenders. Importantly, this study’s findings contradicted previous studies’ findings, highlighting a need for further research on the relationship between adjustment and depressed mood in the correctional environment. Previous offender studies have also yielded unexpected results (Rogers, 2019), suggesting that Lazarus’s (1999) assertion that the effectiveness of coping strategies depends on their context may also apply to other factors, such as adjustment and social support. Finally, this study can contribute to the future development of targeted screening and intervention programmes within the correctional environment to bolster offenders’ mental health.

Footnotes

1

The BDI-II measures characteristic attitudes and symptoms of depression. In this study, ‘Depressed Mood’ is used to indicate the outcome on the BDI-II, including characteristic symptoms in addition to mood disturbance (i.e. sleep disturbances, weight changes, psychomotor agitation, fatigue or loss of energy, diminished ability to concentrate).

Ethical standards

Declaration of conflicts of interest

Ruben Langenhoven has declared no conflicts of interest.

Jacques Jordaan has declared no conflicts of interest.

Anni Hesselink has declared no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (General and Human Research Ethics Committee [GHREC] at the University of the Free State with ethics code: UFS-HSD2022/1630) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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