Relationship Between Self-Directed Learning Readiness and Student Stress Among First-Year Undergraduate Students in Private Universities in Lang’ata Sub-County, Nairobi, Kenya

Relationship Between Self-Directed Learning Readiness and Student Stress Among First-Year Undergraduate Students in Private Universities in Lang’ata Sub-County, Nairobi, Kenya

Kitetu Annah Mukeli., Henry Tucholski PhD., Phyllis Muraya PhD

Tangaza University, Karen, Nairobi

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000594

Received: 20 August 2025; Accepted: 27 August 2025; Published: 23 September 2025

ABSTRACT

First-year university students often face serious challenges adapting from the structured, teacher-led instruction of high school to the autonomy required in self-directed learning environments. This abrupt academic shift frequently leads to increased stress levels, with implications for both academic performance and student well-being. Addressing this issue requires a focused examination of the link between students’ readiness for self-directed learning and the stress they experience. This study investigated the relationship between self-directed learning readiness and student stress among first-year undergraduates in selected private universities in Lang’ata Sub-county, Kenya. Guided by Self-Determination and Self-Efficacy theories, the study adopted a quantitative correlational design. Data were collected using the Self-Directed Learning Readiness Scale and the Student Stress Inventory from a sample of 387 students selected through stratified random sampling. Results revealed a weak statistically significant positive correlation between self-directed learning readiness and student stress (r = .231, p < .001), indicating that higher readiness may be associated with increased stress possibly due to contextual challenges or lack of support systems. These findings offer important insights into the stressors of academic independence and underscore the need for targeted support strategies for first-year students in private universities.

Keywords: Self-directed learning readiness, student stress, first-year undergraduate student, private university.

INTRODUCTION 

In recent years, private universities have gained significant popularity in higher education, drawing a varied range of students with varying backgrounds and motivations. As students transition from high school to university, they often face new challenges that could lead to increased stress. In teacher-directed environments, students rely heavily on structured guidance, clear instructions, and external motivation (Barbayannis et al., 2022). However, in self-directed learning, students assume control over their educational journey, setting objectives, managing their time, and finding intrinsic motivation (Chen et al.,2023).

Self-directed learning readiness (SDLR) involves students creating personalized learning plans to meet their academic needs by using available resources with autonomy and accountability (Barbayannis et al., 2022). It reflects a combination of attitudes, skills, and traits essential for self-directed learning (SDL), including the ability to identify needs, set goals, seek resources, apply strategies, and evaluate progress independently or with minimal guidance (Chakraborty et al., 2023). SDL enables students to manage complex tasks and apply knowledge effectively. It also fosters greater effort, deeper understanding, and a shift from passive learning to active ownership of their educational journey (et al., 2024).

Conversely, student stress occurs when academic, relational, psychological, or physical demands surpass a student’s coping capacity. It manifests as negative emotional, cognitive, behavioral, and physiological reactions to pressure (Mayya et al., 2022).  High stress levels can lead to depression, insomnia, substance abuse, self-harm, and suicidal thoughts, all of which hinder learning.  Stressors include heavy workloads, fear of failure, financial strain, poor time management, and pressure to memorize without understanding (Mayya et al., 2022). Prolonged stress may cause anxiety, burnout, and emotional exhaustion, affecting students’ ability to manage responsibilities (Barbayannis et al., 2022).

Notably, self-directed learning readiness is increasingly recognized as a crucial factor in academic success among first-year undergraduate students. It empowers learners to take initiative, regulate their learning, and adapt more effectively to the demands of higher education (Premkumar et al., 2018). Research suggests that students with high SDLR tend to experience lower levels of stress and anxiety during the transition to university, as they are better equipped to navigate unfamiliar academic environments (Timmermans, 2023). Understanding this relationship can support the development of lifelong learning habits, improve academic performance, and enhance students’ ability to manage stress.

Globally, a study by Manjunath et al. (2024),was conducted in India between 2021 and 2022 among 100 first-year undergraduate students (28 male and 72 female) on the levels of SDLR.  The study reported a median SDLR score of 149, with 46% scoring above 150 and 54% below the acceptable readiness level. Similarly, at the regional level, Ojekou et al. (2019), examined 229 undergraduate nursing students in South-West Nigeria and found an average SDLR score of 203 ± 23.0. Equally, locally, Abiri et al. (2024), studied 404 nursing students from Bondo and Siaya campuses of the Kenya Medical Training College, reporting that 73.5% of the students demonstrated a high level of SDLR, with an average score of 157. Female participants made up 67.1% (n = 271), while males accounted for 32.9% (n = 133). These findings suggest that while SDLR levels vary across contexts, the concept remains a crucial indicator of academic readiness among undergraduate students.

Self-directed learning in higher education is often shaped by how students experience and respond to stress. As Sakitri (2020) highlights, the growing challenges within academic environments influence whether stress becomes a motivating force or a barrier to learning. When students perceive stress as a threat, it can lead to serious consequences such as anxiety, depression, social withdrawal, and even suicidal thoughts. Building on this, Scot (2024) denotes that unmanaged stress can harm students’ health, strain their relationships, and undermine academic success.

Across the globe, student stress has become a growing concern. In the United States, Garett et al. (2017), found that first-year college students experienced moderate stress levels. Similarly, in Canada, Jackson and Serenko (2023) reported high levels of loneliness, anxiety, and health-related worries among university students. In South Africa, Mutinta (2022) pointed out widespread mental distress among undergraduates across several institutions. Closer to home, Josiah et al. (2018) observed that many students at the University of Nairobi faced moderate to high levels of stress. Together, these findings underscore the global nature of student stress and its profound impact on well-being and academic readiness.

Self-directed learning readiness is associated with how students cope with stress across different contexts. In China, Li et al. (2022), found that students with strong SDLR engaged in self-monitoring behaviors that improved both academic performance and mental well-being. This finding aligns with broader research highlighting the importance of strengthening SDLR to support students’ resilience and academic success. Similar conclusions were drawn by Tomas and Protos (2023) and Makhubele (2024), who underscored that students with higher SDLR tend to manage academic demands more effectively, while those with lower SDLR are more vulnerable to stress.

The existing literature reveals a significant gap regarding the relationship between SDLR and student stress (SS) among undergraduate students in selected private universities, particularly within Lang’ata Sub-county, Kenya. At present, there is limited research that specifically examines this important relationship in such academic environments. Although studies from other regions have demonstrated an association, similar research in Kenyan private universities remains sparse.

The study was grounded in Self-Determination Theory (SDT), developed by Deci and Ryan (1985), which provides a psychological framework for understanding motivation and its role in learning. SDT emphasizes the importance of intrinsic motivation, where individuals engage in activities out of personal interest and satisfaction. It distinguishes between autonomous motivation, where learning is self-initiated, and controlled motivation, which is driven by external pressures or rewards (Werth & Williams, 2021). According to Li et al. (2022), students with strong SDLR are more likely to engage in self-monitoring and independent learning behaviors, leading to improved academic and mental outcomes. The theory further posits that meeting basic psychological needs, such as autonomy, competence, and relatedness, enhances self-directed behaviors (Deci & Ryan, 2015). Within this study, SDT offers a valuable lens through which to understand how students’ motivation influences their readiness for self-directed learning and their ability to manage stress.

To complement SDT in this study, Self-Efficacy Theory (SET) was introduced as a second theory to deepen the understanding of students perceived capability to manage academic challenges. Developed by Albert Bandura during his tenure at Stanford University (Garrido, 2023), SET emphasizes that belief in one’s ability to succeed significantly influences motivation, resilience, and performance (Bhati & Sethy, 2020). This theory highlights how students’ confidence in managing tasks, adapting to new environments, and overcoming setbacks shapes their academic outcomes and emotional well-being (Cherry, 2024). Unlike SDT, which focuses on the quality of motivation, SET centers on the confidence to act, making it particularly relevant to student stress and coping (Tavakoly et al., 2024). Students with strong self-efficacy tend to manage stress better and persist through academic challenges, while those with low self-efficacy often experience heightened stress and decreased academic engagement (Betterhelp Editorial Team, 2024). Thus, integrating SET provides a more comprehensive view of how motivation and belief interact to influence SDLR and SS.

Therefore, this study aimed to fill this gap by examining the relationship between SDLR and SS among undergraduate students in selected private universities in Lang’ata Sub-county, Kenya. Specifically, the study sought to answer the following research questions:

RQ1:   What were the levels of SDLR among first-year undergraduates?

RQ2 :   What were the levels of student stress among first-year undergraduates?
RQ3:   What was the relationship between SDLR and student stress?

METHODOLOGY  

The study was grounded in a positivist epistemological perspective, which privileges objective knowledge derived from empirical observation and scientific measurement (Brown, n.d.). Consistent with this philosophical orientation, the research adopted a quantitative paradigm and employed a correlational survey design. This design was deemed appropriate for investigating the nature and strength of relationships between variables while minimizing researcher subjectivity and bias.

The study was guided by the following specific objectives:

1. To determine the levels of self-directed learning readiness among first-year undergraduates.
2. To examine the levels of student stress among first-year undergraduates.
3. To measure the relationship between self-directed learning and student stress among the first-year undergraduates.

The target population comprised 4,217 first-year undergraduate students from selected private universities within Lang’ata Sub-county namely: The Catholic University of Eastern Africa (CUEA), Riara University, Strathmore University, and Tangaza University.  To enhance the study’s methodological rigor and ensure data integrity, explicit inclusion and exclusion criteria were established as presented in table 1 below:

Table 1: Inclusion and Exclusion Criteria

Employing Krejcie and Morgan’s (1970) formula for sample size determination, the study targeted 387 participants; ultimately, structured data was successfully collected from 382 respondents.

Ethical Approval

Ethical approval for the study was granted by the Tangaza University Ethical Review Committee (TUREC) and subsequently by the National Commission for Science, Technology, and Innovation (NACOSTI). All research procedures adhered strictly to the ethical standards prescribed by these bodies, ensuring the confidentiality, anonymity, and protection of participants throughout the data collection process. The researcher affirms the absence of any conflicts of interest, personal, financial, or professional, that could have compromised the integrity of the research or its dissemination.

Data Availability

Data collection was conducted using two standardized instruments: the Self-Directed Learning Readiness Scale (Guglielmino, 1977), comprising 58 Likert-scale items, and the Student Stress Inventory (Gadzella, 1991), containing 40 items. Prior to the main study, a pretest involving 39 full-time students from the School of Business at Daystar University, representing approximately 10% of the intended sample size, was conducted to assess the clarity, relevance, and reliability of the instruments. The Self-Directed Learning Readiness Scale demonstrated excellent internal consistency with a Cronbach’s alpha coefficient of .966, while the Student Stress Inventory exhibited strong reliability with a coefficient of .88. These results confirmed the psychometric robustness of the instruments and their suitability for the primary study. Data analysis was performed using SPSS version 25. The dataset that supports the findings of this research is available from the corresponding author upon reasonable request, in accordance with ethical guidelines and data protection standards.

RESULTS

This section presents the findings of the study in three stages. First, it outlines the socio-demographic characteristics of the participants. Next, it describes the levels of self-directed learning readiness (SDLR) and student stress (SS) among first-year undergraduate students. This step provides essential context for understanding the distribution of each variable, which is crucial for conducting a valid correlation analysis. Finally, the section presents the results of the Pearson correlation analysis used to examine the relationship between SDLR and SS.

Socio-Demographic Characteristics of Participants

The study gathered key background information on participants to contextualize the findings. These socio-demographic variables included age, gender, university attended, mode of study, place of residence, and perceived family support. Table 2 summarizes the distribution of these characteristics across the sample.

Table 2: Demographic Characteristics of Participants

As shown in Table 2, the sample was largely composed of younger students, with over 90% aged between 16 and 30 years. Notably, the gender distribution was nearly equal, with 52.40% female and 47.60%  male. This balance is uncommon in many academic contexts where gender disparities are often observed. In addition, 35.30% of students lived in hostels despite being enrolled in private universities, suggesting a shift toward residential independence even among first-year students. Furthermore, a striking 89.30% of participants reported feeling supported by their families, a factor that may influence how students cope with the demands of university life. These demographic characteristics offer a comprehensive view of the participants, highlighting a predominantly young and supported student population with varied institutional, residential, and academic backgrounds.

Levels of Self-Directed Learning Readiness among Participants

As a preparatory step toward analyzing the relationship between variables, data was first collected and analyzed to determine the levels of self-directed learning readiness among participants. Table 3 presents the distribution of SDLR scores, categorizing students into different levels of readiness.

Table 3: Levels of Self-Directed Learning Readiness

As presented in Table 3, the levels of self-directed learning readiness among participants showed a polarized distribution. The mean SDLR level was 1.97 with a standard deviation of 0.917, reflecting substantial variability within the group and supporting the notion of two distinct subpopulations regarding readiness for self-directed learning. A striking finding is that the majority of students were concentrated on the extremes, with either above-average (40.50%) or below-average (43.50%) levels of readiness. In contrast, only a small minority (16%) demonstrated average levels of SDLR. This pattern suggests a significant divide in students’ preparedness for self-directed learning, with relatively few falling within the moderate range.

Levels of Students Stress among Participants

To measure the relationship of student stress with self-directed learning readiness, data was also collected to assess the levels of student stress among participants. Table 4 summarizes the distribution of stress levels based on the Student Stress Inventory (SSI).

Table 4:  Levels of Student Stress

Table 4 reveals a notable concentration of students experiencing moderate stress, with relatively few reporting either severe or very low levels. The overall mean stress level was 1.84 with a standard deviation of 0.524, suggesting that most students experienced stress levels clustered around the moderate range, with relatively low variability in stress scores among the students. This clustering suggests that while stress is widespread among first-year undergraduates, it tends to remain within a manageable range for most. The relatively small proportion of students at the extremes may point to individual differences in coping mechanisms or institutional support structures that buffer against high stress.

Relationship Between Self-Directed Learning Readiness and Student Stress Among Participants

The objective of this study was to measure the relationship between self-directed learning readiness (SDLR) and student stress (SS) among first-year undergraduate students. To examine this relationship, Pearson Product-Moment Correlation Coefficient was conducted. The scattered plot presented in Figure 1 illustrates the relationship between SDLR and SS among the student population in this study.

Figure 1:  Scattered Plot on the Relationship Between Self-Directed Learning Readiness and Student Stress

The scatterplot in Figure 1 illustrates the relationship between total SDLR scores and total student stress (SS) scores among first-year undergraduate students. The x-axis represents self-directed learning readiness scores, ranging from 58 to 290, reflecting varying levels of students’ preparedness for autonomous learning. The y-axis shows student stress scores, ranging from 40 to 160, indicating different levels of perceived stress among participants. Each dot on the scatterplot corresponds to an individual student’s SDLR and SS scores. The fitted trend line reveals a positive linear relationship, suggesting that, contrary to expectations, students with higher SDLR may also experience slightly higher stress. However, the relationship appears weak and scattered, as the data points are widely dispersed around the regression line. The R² value is 0.053, indicating that only 5.3% of the variance in SS scores can be explained by differences in SDLR. This represents a small effect size, suggesting that although a measurable relationship exists, it is not strong, and other factors may play a more significant role in influencing student stress.

To further measure the relationship between SDLR and SS among first-year undergraduate students, Pearson’s correlation coefficient was used to assess the strength and direction of the association between the total SDLR score and the total SS score. Table 5 presents the results of the Pearson correlation analysis, offering insights into how these two variables were related based on the participants’ responses.

Table 5:Pearson’s Correlation Coefficient for the Relationship between Self-Directed Learning Readiness and Student Stress

A Pearson correlation analysis as shown in Table 5 was conducted to measure the relationship between SDLR and SS among first-year undergraduate students. The results showed a positive and statistically significant correlation, r(382) = 0.231, p < .001, indicating that as SDLR scores increase, SS also tended to increase, albeit modestly. While the correlation was statistically significant at the 0.01 level, the strength of the relationship was weak, as indicated by the coefficient value (r = 0.231). This suggested that although there was a measurable association between the two variables, higher SDLR was only weakly associated with higher SS in this sample.

DISCUSSION

The study examined the demographic characteristics, self-directed learning readiness (SDLR), and levels of student stress among first-year undergraduate students enrolled in private universities within Lang’ata Sub-county, Nairobi. Data collection focused on variables such as age, gender, residence, mode of study, and perceived family support. The age distribution reflected a typical post-secondary transition, with most participants falling within the younger age bracket, consistent with Mutiso et al. (2023), who reported a similar pattern among Kenyan university students. Gender distribution was nearly balanced, though a slight female majority was observed, an outcome that aligns with findings by Manjunath et al. (2024), in India, but diverges from studies in other regions such as Ghana, where Amankwah et al. (2022), reported higher male enrollment. Most students resided in hostels, highlighting the importance of structured living environments during university transition, as similarly noted by Chen et al. (2022). The majority were full-time students and reported strong family support, suggesting a stable psychosocial foundation.

An analysis of SDLR levels revealed a polarized distribution across the student population. Approximately 43.5% of students scored below average (58–201), 16.0% fell within the average range (202–226), and 40.5% scored above average (227–290). This notable divide, with the majority positioned either at the low or high ends of the spectrum, reflected considerable variability, confirmed by a mean score of 1.97 and a standard deviation of 0.917. The findings indicated the presence of two distinct subgroups: those well-prepared to engage in autonomous learning, and those who may struggle significantly with self-direction. Such a bimodal pattern echoes finding by Premkumar et al. (2018) in India, who reported a nearly identical SDLR distribution. In contrast, Sadeghi et al. (2024) in Pakistan found that 61.3% of students demonstrated good SDLR, with only 0.5% falling into the poor readiness category, while Yang et al. (2024) in China reported a high mean self-directed learning ability score. These disparities suggest that readiness for self-directed learning is highly influenced by context, including cultural expectations, educational systems, and institutional support structures.

Theoretically, Self-Determination Theory (SDT) provided a useful lens for interpreting the results. According to SDT, intrinsic motivation and self-directed behavior depend on the satisfaction of three psychological needs: autonomy, competence, and relatedness (Deci & Ryan, 2015; Werth & Williams, 2021). The findings imply that students with high SDLR are likely to experience greater autonomy and competence, fostering strong intrinsic motivation. Conversely, students scoring low on SDLR may face unmet psychological needs, resulting in diminished confidence and lower motivation. Relatedness, perceptions of social support, also plays a crucial role, and discrepancies in the fulfilment of these needs may explain the pronounced gap in SDLR scores.

Regarding levels of stress, the majority of students (70.2%) reported moderate stress, with 22.8% experiencing mild and 7.1% severe levels. This distribution mirrors global patterns, as evidenced by Alkhawaldeh et al. (2023), who found that 75.1% of students in Asia experienced moderate stress, and Josiah et al. (2018), who identified moderate-to-high stress in 64.4% of students at the University of Nairobi. However, comparative studies, such as that by Garett et al. (2017), in the United States, revealed even higher stress levels, indicating that cultural and institutional variables may shape students’ psychological responses. Grounded in Self-Efficacy Theory (Bandura, 1977), the findings suggest that students’ perceptions of their ability to manage academic demands significantly influence their experience of stress. High self-efficacy has been associated with adaptive coping, whereas low self-efficacy may exacerbate stress, particularly in challenging academic settings (Peng, 2023).

To explore the relationship between SDLR and student stress, Pearson’s Product-Moment Correlation Coefficient was computed. The results revealed a weak but statistically significant positive relationship, r(382) = 0.231, p < .001, indicating that higher SDLR was associated with slightly elevated levels of stress. While the correlation was significant, the effect size was small (R² = 5.3%), suggesting that SDLR accounts for only a modest proportion of the variance in stress levels. This limited explanatory power may be attributable to other unmeasured factors such as institutional support systems, socio-economic status, individual coping strategies, and the availability of peer or faculty mentorship. This finding was somewhat counterintuitive, as self-directed learning is generally viewed as a buffer against academic stress. A possible explanation lies in the increased self-regulatory demands imposed on learners with high SDLR, which may heighten anxiety in the absence of adequate institutional support. The finding diverges from previous research, such as Li et al. (2022), who reported a negative association between SDLR and stress among Chinese students, and Chen (2023), who found that students with high SDLR and self-efficacy experienced significantly less academic burnout. These contrasting results suggest that while SDLR is beneficial for promoting autonomy and engagement, it may also contribute to stress if learners face excessive demands or insufficient support systems. In this context, highly self-directed students may become overwhelmed in academic environments that lack clarity, structure, or resources, despite their internal motivation and capabilities.

These findings challenge one of the core assumptions of Self-Determination Theory, that increased autonomy inevitably results in lower stress. The observed positive correlation between SDLR and stress raises important theoretical considerations. In environments characterized by academic novelty, limited institutional support, and inconsistent feedback, such as those encountered by many first-year students in Lang’ata, autonomy may become a source of pressure rather than empowerment. This suggests a need to refine SDT to better account for contextual variability, particularly in under-researched settings like private universities in Kenya. The theory’s original formulation, developed largely in Western and Asian contexts, may not fully capture the dynamics experienced by students in diverse cultural and institutional environments. A context-sensitive revision of SDT could incorporate structural and sociocultural influences, offering a more nuanced understanding of how autonomy, competence, and relatedness interact to shape motivation, readiness, and stress.

Overall, the findings underscore the complex interplay between individual psychological traits and environmental factors in shaping students’ academic experiences. While self-directed learning readiness and intrinsic motivation are essential for academic success, they do not operate in isolation. The presence or absence of supportive institutional structures, such as accessible faculty, effective orientation programs, and robust mental health services, may determine whether autonomy becomes an asset or a burden. As such, fostering student well-being and academic preparedness requires both the cultivation of internal learning capacities and the creation of environments that meaningfully support those capacities.

CONCLUSION

This study explored the demographic characteristics, self-directed learning readiness (SDLR), and student stress levels among first-year undergraduate students in private universities within Lang’ata Sub-county, Nairobi. Most students were young, full-time, and reported strong family support factors that may offer a stable psychosocial base during university transition.

The SDLR findings showed a polarized distribution, with students clustered at either low or high readiness levels. While some students demonstrated strong capacity for autonomous learning, others may struggle without structured support. This variation underscores the need for tailored academic interventions. Theoretical insights from Self-Determination Theory (SDT) suggest that autonomy and competence foster intrinsic motivation, but these benefits are context dependent.

Notably, the study found a weak but statistically significant positive correlation between SDLR and stress, suggesting that higher self-directed learning readiness may be linked to increased stress. This counterintuitive result points to the need for a balanced approach: promoting autonomy while ensuring institutional support systems are in place to prevent undue pressure on highly self-directed students.

In summary, while SDLR is crucial for academic success, it must be nurtured within supportive environments. Student readiness alone does not reduce stress; rather, the interplay between personal motivation and responsive institutional structures determines whether autonomy fosters growth or strain. This study contributes to the literature by showing that in Kenyan private universities, high SDLR may paradoxically increase stress, underscoring the need for context-sensitive support structures.

RECOMMENDATIONS

The study revealed varied levels of self-directed learning readiness (SDLR) among first-year undergraduate students, with a polarized distribution between low and high readiness. To address this, it is recommended that:

• Universities introduce structured orientation programs focusing on goal setting, time management, and independent study skills.
• Students should be encouraged to participate in peer study groups and mentoring relationships, allowing those with higher readiness to support peers who are developing these skills.
• Institutions promote the use of academic advising and learning resources (libraries, online platforms, and workshops) to strengthen students’ autonomy and confidence.

The findings also indicated moderate stress levels among first-year students, underscoring the need for stronger academic and emotional support. To address this, it is recommended that:

• Universities strengthen support systems through teaching staff, advisors, counselors, and mentors.
• Accessible mental health services, structured mentoring, and skill-building opportunities should be made available to students.
• Academic practices emphasize balanced workloads, timely feedback, and supportive teaching approaches.
• Scaffolded academic support should be introduced to ease students’ transition into university learning.

Finally, the study found a weak but statistically significant positive correlation between SDLR and student stress, suggesting that greater autonomy may sometimes increase stress. To address this, it is recommended that:

• Institutions establish tailored support programs such as mentorship, counseling, and stress management workshops aligned with students’ varying levels of SDLR.
• Universities adopt student-centered policies including proactive academic advising, structured learning pathways, and early intervention mechanisms.
• Inclusive teaching practices be prioritized to minimize stress while supporting autonomy.
• Policymakers recognize that both personal motivation and institutional support are necessary for self-directed learning to foster growth rather than strain.

This study was limited by its exclusive focus on private universities within Lang’ata Sub-county and its use of quantitative research methods. While useful for identifying general patterns, this approach may not fully capture the underlying reasons for the observed relationship between self-directed learning readiness and student stress. Future research should consider a broader institutional and geographical scope.  It should also incorporate qualitative or mixed method designs to explore not only the nature of this relationship but also the factors contributing to it, such as academic expectations, personal coping strategies, and institutional support systems.

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