Emotional Support as a Lifeline: Promoting the Sustainability of Quality of Life for College Students with Disabilities Facing Mental Health Disorders

Abstract

Drawing on Social Support Theory, this research makes a new attempt to examine the moderating role of emotional support for students with disability in the link from mental health disorder to a sustained quality of life. Responses from 620 students with disabilities were analyzed with SEM analysis using Smart PLS. The results showed a direct negative impact of stress, depression, and anxiety on the QoL of disabled students. Additionally, the results confirmed a moderating role of emotional support in the link between stress, anxiety, and QoL among disabled students. This means that emotional support was able to mitigate the negative impact of both stress and anxiety on the QoL of disabled students. However, the results did not confirm the moderate role of depression in this relationship. This means that emotional support given to students with disabilities was not enough to mitigate the negative impact of depression on the sustained QoL of disabled students. It also means that there are other support structures and interventions needed to mitigate the negative impact of depression on the QoL among disabled students. Implications of the results are thus elaborated.

1. Introduction

Quality of life (QoL) has become a major concern for policymakers and researchers in recent decades. QoL was defined by the World Health Organization (WHO) as “individuals’ perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” [1]. QoL includes a group of life conditions and satisfaction. This life condition and satisfaction includes physical, psychological, and social aspects [2].

In the context of people with disability, QoL is a more complex and sophisticated issue as it encompasses their well-being and satisfaction with life [3]. QoL has been affected by many issues, such as mental health [4,5], self-efficacy [4], physical fitness, self-esteem, and academic performance [3], disability service support, family support, and friends’ support [6].

The link between mental health (MH) and QoL, especially among disabled students, was raised recently, albeit by a few scholars, as an important concern [4,5]. MH was identified as a vital issue, especially for disabled students [5,7]. MH is defined as the status of mental well-being, which qualifies individuals to cope with life stress, recognize their abilities, and work with others [8]. It is a side of an individual’s health that affects their abilities to make decisions, work with others and shape their own world. It was identified as basic human rights. The level and degree of MH varies from one to another that could be beyond the mental disorder and hence would have different social and clinical outcomes [8].

MH would become a more critical health concern among disabled people as studies [4,5] revealed that disabled people would experience MH disorders more than other people [9]. This is because disabled people are more likely to suffer from discrimination and stigmatization than other people, which could affect their mental health [10]. They often suffer from accessibility due to physical fitness as well as social isolation, which increases stress more than other people [9]. Furthermore, they could have chronic pain and some health conditions that also increase the risk of depression and anxiety [6].

Even though there are growing studies [4,5,6] on the direct or indirect link between MH and QoL, particularly among those students with disabilities, limited studies to date have addressed the role of support, especially emotional ones, in mitigating the negative effect of mental health among disabled health, which could happen due to various reasons, on their QoL [5,6,11]. For example, Al-Shaer et al. [6] examined the moderating role of social connectedness on the relationship between the three dimensions of MH and QoL. They found that social connectedness was able to moderate the effect of stress on QoL but failed to mitigate the negative influence of depression and anxiety on QoL among disabled students. In the same context, Moustafa et al. [5] found that student support services that were provided to disabled students at Saudi universities were able to alleviate the negative effect of MH disorder on the QoL. Notwithstanding this, none of the published studies to date, to the best of the knowledge of the research team, have addressed the moderating role of emotional support given to disabled people, particularly students, in the link between MH and their QoL. This study bridges this research gap and informs families, university disability units, and tutors on how they could deal with disabled students to ensure their QoL.

This study builds on Social Support Theory (SST) [12], which reveals that social support, especially emotional ones, significantly affects MH as it protects people from negative consequences of stress, anxiety and depression [5]. Emotional support (ES) systems, as a main pillar of social sustainability, can promote equitable, inclusive, settings, ensuring that disabled students can thrive for long term. This could be more important for disabled students, who could experience more MH disorders than other students. The theory assumes that different forms of social support, including emotional, informational and companionship support, could mitigate the challenges that students with disabilities face and encourage their well-being and, ultimately, their quality of life. Providing emotional support to disabled students in forms of empathy, concern, love, trust, acceptance, care, and encouragement made them feel valued and cared and loved [13]. Emotional support can be employed to mitigate the adverse influence of stress by offering methods to effectively cope with stressful circumstances; e.g., a strong support network can help students restructure stressful situations, minimizing the perception of severity and fostering resilience [14]. Emotional support can also mitigate anxiety perception by offering a feeling of belonging and security. Knowing that peer students are available to offer assistance can minimize the feelings of fear and uncertainty [15]. Supportive and social relationships as well encourage high positive adaptive strategies, such as problem solving or finding help, which might minimize anxiety feelings [16]. SST indicates that emotional support and friendship might help counteract the symptoms of depression by promoting a feeling of purpose and connection [17]. Emotional support is located at the core of SST and plays a key role in mitigating the relationships between anxiety, stress, depression, and QoL [18]. Hence, this research examines whether emotional support given to students with a disability could mitigate the negative impact of MH on quality of their life. This study is directly relevant to UN “Sustainable Development Goals” (SDGs), specifically, SDG 3 “Good Health and Well-being” and SDG 4 “Quality Education”, by highlighting mental health barriers to sustain equitable education opportunities. The next section of this manuscript discusses research hypotheses. It then presents the methods adopted for collecting and analyzing the data. This is followed by presenting and discussing the research findings. The manuscripts are concluded with final remarks and future research opportunities.

2. Conceptual Framework and Hypothesis Building

2.1. MH and QoL Among Students with Disabilities

MH is defined as a “state of well-being in which the individual realizes his or her abilities, can cope with the normal stresses of life, can work productively and fruitfully, and can contribute to his or her community” [8]. One the other side, QoL is defined as “an individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” [1]. This includes several indicators, e.g., employment, wealth, physical and mental health. The link between health, in general, MH, in particular, and QoL is well established [5,19]. The concept of MH goes back to the 19th century when the term “mental hygiene” was proposed by William Sweetster, which was then replaced by the term MH in the beginning of the 20th century to reflect a preventive area of healthcare, not a treatment of illness [20].

The current research focuses on the three main dimensions of mental health disorder (MHD): depression, anxiety, and stress and their linkage with QoL among university students with disability. Stress is a general symptom among students due to their experience of new academic life that differs from their high school. However, in the context of students with a disability, stress could result in their disability. Stress could be a response to stressors, which lead to mental discomfort. Despite the fact that stress is normal among people, high levels of stress could lead to heart attack, ulcers, and mental illness such as depression [21]. Students who experience high levels of stress were found to have less sleep times and decreased their life satisfaction [22]. Stress among students with disability was found to negatively affect their academic life [23]. Recent studies [5,6,11] showed that stress has direct negative influences on QoL among students with disabilities. Thus, we argue the following:

H1: 

Stress decreases the QoL perceptions among students with disability.

Depression reflects a low mood accompanied with an aversion of activity and influences an individual’s behavior and sense of well-being [24]. Depression can result from physical diseases or disability and often be in the form of a mood disorder [25]. Depression could have a negative effect on relationships with peers and family members [26]. People in depression often lack motivation, enthusiasm, concertation, feelings of joy, experience sadness, have feelings of hopelessness, experience changes in their appetite, and stay isolated [27]. It was argued that different forms of disability could lead to depression [28]. Students with disability who experience depression could have difficulty in eating, sleeping and studying; hence, they show poor academic performance [29]. Unsurprisingly, then, the negative association between depression among students with disability and their QoL was confirmed in a number of recent studies [5,6,11]. These studies confirmed that depression negatively affects the QoL of students with disability. Therefore, we assume the following:

H2: 

Depression decreases the QoL perceptions among students with disability.

Anxiety is another MH disorder that many people around the world suffer from. It is also more common among people with disability. Anxiety refers to the feeling of worry that was not focused on certain situations but is of anticipated events [30]. This feeling of anxiety is associated with other negative behaviors such as rumination [31]. It also could be accompanied by several physical and psychological negative states, e.g., somatic complaints, nausea, fatigue, and problems in concentration [25]. It was confirmed that people who experience anxiety would not continue in any situations that require effort, especially if they faced bad experiences with similar situations [32]. Therefore, students with disability, who could experience anxiety, would suffer in their academic life, which also negatively affect their QoL [5]. It is approved that the spread of anxiety among students with disabilities would certainly affect their QoL as their unpleasant state of inner turmoil would have a negative effect on their QoL. Thus, we assume the following:

H3: 

Anxiety decreases the QoL perceptions among students with disability.

2.2. The Moderating Role of Emotional Support

The negative relationship between the three dimensions of MHD: depression, anxiety and stress and QoL among students with disabilities was confirmed in earlier research [5,6,11]. However, limited studies, to date, addressed the factors that could mitigate the negative impact of depression, anxiety and stress on QoL among students with disabilities. Hence, this study examines the moderating role of emotional support on the link between three dimensions of MHD: depression, anxiety and stress and QoL among students with disabilities. This is because the emotional support given to students with disabilities formed by their families, colleagues, and tutors can help them deal with MHD and enhance their QoL as the SST implies [12]. Al-Shaer et al. [6] found that social connectedness moderates the link between stress and QoL among students with disabilities but did not have a moderation role on the link between depression, anxiety, and the QoL of disabled students. However, another recent study on disabled students showed that support services provided by universities to disabled students moderate the link between depression, anxiety, stress, and their QoL. This means that this university support enabled disabled students to mitigate the negative impact of MHD dimensions on QoL. Thus, we argue the following:

H4: 

Emotional support of students with disabilities mitigates the negative impact of stress on their QoL.

H5: 

Emotional support of students with disabilities mitigates the negative impact of depression on their QoL.

H6: 

Emotional support of students with disabilities mitigates the negative impact of anxiety on their QoL.

3. Methods

3.1. Study Measures

The proposed theoretical model of this research was tested employing a structured survey approach. The research scale was based on previous measurements. The questionnaire was designed to have different interrelated sections. The first section has an introduction about the study aim and objectives and a statement to obtain participation consent from the targeted respondents. The second section aimed to collect demographic characteristics including age, study level, disability type, and gender. The last section was designed to measure the study variables including the independent, dependent and moderating variables. To measure disabled students’ mental health disorder, we employed the Depression (Deprsin), Anxiety (Enzity), and Stress (Stres) 21–Items Scale (DASS-21), which assesses mental health disorder symptoms with 21 items. The DASS-21 scale is the short version of the DASS scale designed by Lovibond and Lovibond [33]. This shortened version is still widely adaptable for use by different scholars in various settings (i.e., [6]). DASS-21 was developed to evaluate an individual’s negative emotions over the past 7 days, and the scale has three main factors with subfactors for each, containing seven variables. Respondents were invited to evaluate the level of the agreement with each variable with a four-level Likert scoring approach from 0 “indicating no agreement” to 3 “indicating a high level of agreement” [33]. The DASS-21 scale was widely employed in previous studies and showed good reliability and validity [34,35,36,37]. The dependent “quality of life” factor was measured in our study using the widely known scale: “Satisfaction with Life Scale” (SWLS). The SWLS was first developed by Diener et al. [38] and created to be a multidimensional scale with five items. The SWLS is designed to assess students’ cognitive evaluation of life satisfaction. Students were asked about their level of agreement of different statements related to their life happiness. Each item was measured with 5-point Likert scale, ranging from 1 “strong disagreement” to 5 “strong agreement”. Finally, the perception of emotional support (PES) was operationalized by a 7-item multidimensional scale developed by Shakespeare-Finch and Obst [39]. The complete study scale is attached to Appendix A.

As our paper employed scales from previous research papers based on an extensive review of the literature, constructing validity of the designed instruments can be justified. As per the face validity, five practitioners assessed each question, and the complete questionnaire was pre-piloted with 12 disabled students in King Faisal University. Only a few slight changes to the wording were revised, but the questionnaire and concepts employed were interpreted properly and understandable. Thus, it can be inferred that the employed questionnaire achieved face and content validity.

3.2. Data Collection and Sampling

According to the latest available statistics, the percentage of people with disabilities (mobility, visual, hearing, communication, self-care, and memory) in Saudi Arabia (KSA) is around 1.8%, representing 64,800,000 individuals out of a total population of 36,000,000. A total of 58% of them are at university level age (37,584,000) [40]. We targeted data collection from universities that represent the five main areas of KSA: King Faisal University (east); Umm Al-Qura University (west); Imam Mohammad Ibn Saud Islamic University (center); Jazan University (south); and Northern Border University (north). In order to collect data from the target students, the questionnaire was disseminated through social media and official university email channels during May and June of 2024. The purpose of this study was to investigate the effects of mental health disorders on the quality of life (QoL) of disabled students, looking specifically at the moderating impact of perceived emotional support. The participants were sampled on a nonprobability basis using a convenience sampling. To assist in data collection, 50 enumerators were recruited. Enumerators then attended an orientation session introducing them to the objectives and ethical standards for disabled students. They were trained in how to obtain informed consent from participants and address any questions or concerns before surveying. This briefing also laid emphasis on sensitivity, respect, and confidentiality, as well as provided guidance on handling sensitive data and protecting participant privacy. A total of 1000 responses were obtained, out of which 620 were validated after a thorough review, giving a commendable response rate of 62%. The final dataset was analyzed on both measurement and structural models using the Partial Least Squares (PLS) path modeling technique, an emerging strategy within the structural equation modeling (SEM) framework. The demographic distribution of respondents shows diversity within the sample. Gender representation was fairly balanced since there was a 57% representation of male respondents as compared to 43% females. The majority (55%) of the respondents were aged between 18 and 25 years. In terms of disability type, the most common category was physical (mobility) impairment (33%), followed by communication disability (22%) and visual impairments (20%). This demographic diversity highlights the sample’s representativeness and its value for discovering the interrelationship between disability type, perceived emotional support, and QoL.

When both independent and dependent questions are answered by the same source (participant), the possibility of common method variance (CMV) arises [41]. CMV is an increasingly known issue in research, especially in those studies using self-reported data collected from surveys [42] since it may warrant effects on construct validity between two or more constructs [43,44]. Thus, Reio identified two such main approaches to overcoming it: the improvement of procedural design and statistical control application. This study took several procedural measures from Podsakoff et al. [45] to minimize any CMV in the questionnaire design. In order to achieve these aims, they included reducing response biases, for example, agreement biases, affirmative or negative, and the use of common scale formats; lessening variable priming effects; having the right dose of questionnaire approaches where possible; and, finally, not presenting variables in any ways that might seem to incur characteristic response patterns or common answer effects. Besides these, the study confirmed the absence of significant CMV by employing Harman’s one-factor test as a statistical safeguard. Results of this analysis yielded in confirmation that there was no such major issue concerning CMV in this research, thus validating the relationships found among the constructs.

4. Data Analysis Technique

The Partial Least Squares (PLS) method was chosen for data analysis as it aligns with this study’s objective design. PLS has three main advantages over other SEM techniques: especially ideal for developing theory-oriented models [46], viable for cause–effect analysis [47], and less stringent on assumptions regarding sample size and data normality [48]. These benefits made PLS appropriate for this study. The analysis was conducted using SmartPLS 4.0 software [49]. We tested the endogeneity issues that might arise in our model due to reverse causality, omitted variable bias, and then adopted Henseler et al.’s [48] recommendations of a two-tier procedure analysis of a PLS-SEM model, with the first tier as measuring model quality in terms of validity and reliability and followed by testing the structural model for significant path and hypothesis examination.

5. Results

5.1. Assessing Endogeneity in the Research Model

Endogeneity in research can have various causes, such as simultaneous causality, measure errors, common method variance, and observed/unobserved heterogeneity. However, endogeneity issues are most regularly rooted from omitted variables that might be correlated with one or other independent variables in the path model [50]. Omitting such items causes a correlation among the related independent variables and the dependent variables’ error. This might infer that the independent variables are not only predicting the dependent variable but also the error terms in the model. The Gaussian Copula method permits researchers to test and detect endogeneity in PLS-SEM (i.e., for paths in the structural model) [51,52,53]. As suggested by Hult et al. [50], we run the model using 5000 bootstrapped subsamples to estimate the model, including the Gaussian copula. As shown in

Table 1. Test of endogeneity using Gaussian Copula in PLS-SEM.

One single Gaussian Copula
	β	t	p
GC (Stress) -> Quality of Life	0.161	1.020	0.093
GC (Depression) -> Quality of Life	0.096	1.883	0.072
GC (Anxiety) -> Quality of Life	−0.172	1.084	0.278
Two Gaussian Copula
First combination
GC (Stress) -> Quality of Life	0.171	1.436	0.065
GC (Depression) -> Quality of Life	0.109	1.348	0.056
Second combination
GC (Stress) -> Quality of Life	0.147	1.862	0.074
GC (Anxiety) -> Quality of Life	−0.102	0.656	0.512
Third Combination
GC (Anxiety) -> Quality of Life	−0.276	1.847	0.065
GC (Depression) -> Quality of Life	0.113	1.190	0.059
Three Gaussian Copula
GC (Stress) -> Quality of Life	0.136	1.152	0.071
GC (Depression) -> Quality of Life	0.105	1.589	0.080
GC (Anxiety) -> Quality of Life	−0.214	1.433	0.152

, all the p values (for one single Gaussian Copula, two Gaussian Copula, and three Gaussian Copula combinations) are insignificant, which indicates the absence of endogeneity in our proposed model.

5.2. Tier One: Measurement Model Results for Validity and Relaibilty

We evaluated the measurement model for reliability and validity, as per Hair et al. [47], suggestions using indicator coefficients, composite reliability (CR), and the average variance extracted (AVE) for each construct. Table 1 provides the model’s outer loadings, CR, Cronbach’s α, and the AVE. The item reliability revealed satisfactory results because all the reflective factor loadings exceeded the commendable value of 0.5 [46], with their values ranging from 0.845 to 0.948. In terms of CR values, all constructs surpassed the minimum requisite of 0.70 as stipulated by Hair et al. [47], with depression recording the highest value of 0.967 and quality of life (QoL) at the lowest value of 0.939. The model, therefore, meets the criteria for construct reliability. As per convergent validity results, the AVE values in

Table 2. Psychometric properties of the study scale.

Measure	Variable	Loading	CR	Cronbach’s a	AVE	VIF
Depression			0.967	0.970	0.835
	Deprsin1	0.919				4.758
	Deprsin2	0.911				4.331
	Deprsin3	0.919				4.968
	Deprsin4	0.924				4.494
	Deprsin5	0.911				4.633
	Deprsin6	0.908				4.356
	Deprsin6	0.904				4.047
Anxiety			0.962	0.957	0.795
	Enzity1	0.871				4.051
	Enzity2	0.900				4.637
	Enzity3	0.874				3.885
	Enzity4	0.880				4.745
	Enzity5	0.910				4.401
	Enzity6	0.902				4.122
	Enzity7	0.904				4.328
Stress			0.949	0.961	0.763
	Stress1	0.825				2.499
	Stress2	0.894				4.733
	Stress3	0.865				4.235
	Stress4	0.890				3.854
	Stress5	0.886				3.678
	Stress6	0.893				4.152
	Stress7	0.860				3.580
PES			0.971	0.939	0.804
	PES1	0.936				2.291
	PES2	0.934				4.083
	PES3	0.908				2.261
	PES4	0.913				4.244
	PES5	0.933				3.779
	PES6	0.948				4.046
	PES7	0.880				2.727
QoL			0.939	0.961	0.765
	QoLif1	0.915				4.355
	QoLif2	0.901				3.742
	QoLif3	0.910				4.001
	QoLif4	0.911				3.796
	QoLif5	0.845				2.335

, are all above the threshold of 0.50, the minimum suggested by Fornell and Larcker [54]. The last criterion of assessment was to ensure discriminant validity by employing indicator analysis [54], the HTMT “heterotrait–monotrait ratio of correlations”, which should be below 0.90 [48] as shown in

Table 3. Heterotrait–monotrait ratio (HTMT) matrix.

	Anxiety	Depression	PES	Quality of Life	Stress
Anxiety
Depression	0.680
PES	0.355	0.522
Quality of Life	0.486	0.415	0.240
Stress	0.614	0.330	0.127	0.379
PES x Anxiety	0.362	0.510	0.190	0.468	0.074

, as well as cross-loadings to confirm that all constructs load strongly on their respective construct. In addition, the last column of Table 2 shows that all VIF scores are well below threshold 5, which confirms that the model is free from multicollinearity.

Table 3 indicated that all the AVE square-foot scores (diagonal values) exceeded the correlations below the diagonal. This signaled that each factor owed more variance to its predetermined variables than any other factor in the model [48].

Table 4. Cross loadings.

	Anxiety	Depression	PES	Quality of Life	Stress
Deprsin1	0.613	0.919	0.468	−0.366	0.330
Deprsin2	0.575	0.911	0.462	−0.391	0.280
Deprsin3	0.626	0.919	0.435	−0.362	0.315
Deprsin4	0.591	0.924	0.484	−0.319	0.290
Deprsin5	0.573	0.911	0.471	−0.331	0.256
Deprsin6	0.581	0.908	0.425	−0.360	0.282
Deprsin7	0.629	0.904	0.458	−0.404	0.326
Enzity1	0.871	0.563	0.260	−0.391	0.454
Enzity2	0.900	0.605	0.213	−0.500	0.517
Enzity3	0.874	0.567	0.385	−0.387	0.527
Enzity4	0.880	0.567	0.352	−0.363	0.553
Enzity5	0.910	0.613	0.381	−0.396	0.569
Enzity6	0.902	0.577	0.252	−0.429	0.519
Enzity7	0.904	0.597	0.264	−0.422	0.586
PES1	0.282	0.445	0.936	0.244	0.061
PES2	0.311	0.456	0.934	0.226	0.090
PES4	0.332	0.472	0.913	0.187	0.163
PES5	0.315	0.456	0.933	0.220	0.088
PES6	0.273	0.443	0.948	0.262	0.074
PES7	0.330	0.504	0.880	0.164	0.158
PES3	0.331	0.486	0.908	0.181	0.157
QoLif1	−0.379	−0.346	0.216	0.915	−0.293
QoLif2	−0.414	−0.369	0.189	0.901	−0.330
QoLif3	−0.413	−0.347	0.213	0.910	−0.305
QoLif4	−0.410	−0.376	0.196	0.911	−0.307
QoLif5	−0.468	−0.347	0.235	0.845	−0.417
Stress1	0.628	0.408	0.177	−0.345	0.825
Stress2	0.479	0.235	0.085	−0.292	0.894
Stress3	0.380	0.182	0.034	−0.227	0.865
Stress4	0.554	0.332	0.172	−0.309	0.890
Stress5	0.508	0.273	0.130	−0.286	0.886
Stress6	0.579	0.312	0.057	−0.416	0.893
Stress7	0.446	0.202	0.045	−0.327	0.860

also verify discriminant validity as no cross-loading was found, and each item loads highly to its factor. The results show satisfactory discriminant validity on the construct and the item level.

5.3. Tier-Two: Structural Model Results

The relationships in the justified conceptual model were evaluated using 5000 bootstrapped subsamples. The properties of the study model were assessed following the criteria outlined by Henseler et al. [48], which consist of the “coefficient of determination” (R²) and “Stone-Geisser’s” Q². Furthermore, the “standardized root-mean-square residual” (SRMR) was used as an indicator of model good fit. The analysis yielded an SRMR value of 0.056, which is considered acceptable. For R², a value of 0.583 was attained for QoL, which, based on the recommendations of Henseler et al. [48], is considered high and acceptable. Additionally, the Q² values, which signal the model’s predictive capacity, were reported at 0.550, exceeding zero and representing strong predictive relevance [47]. The significant path coefficients, combined with the high R² and Q² values, highlight the model’s robust explanatory power, strong predictive relevance, and suitability for the variables under study. This provides a solid foundation for analyzing the findings derived from the model.

The practical results are shown in Figure 1 and

Table 5. Results of the hypothesis assessment.

Relationships	β	t	p
H1: Stress -> Quality of Life (Accepted)	−0.147	2.320	0.020
H2: Depression -> Quality of Life (Accepted)	−0.288	4.597	0.000
H3: Anxiety -> Quality of Life (Accepted)	−0.270	3.428	0.001
H4: PES × Stress -> Quality of Life (Accepted)	0.139	2.460	0.014
H5: PES × Depression -> Quality of Life (Rejected)	0.056	0.931	0.352
H6: PES × Anxiety -> Quality of Life (Accepted)	0.205	3.026	0.002

and highlighted all the tested paths after conducting the bootstrap 5000 times. The output showed that the three dimensions of mental health disorders significantly and negatively affect the QoL of disabled students in KSA universities. More specifically, stress (as a dimension of mental health disorder) has a significant negative (path coefficient = −0.147, t = 2.320, p < 0.001) impact on QoL. Similarly, depression (as a dimension of mental health disorder) has a significant negative (path coefficient = −0.288, t = 4.597, p < 0.001) impact on QoL. Likewise, anxiety (as a dimension of mental health disorder) has a significant negative (path coefficient = −0.270, t = 3.428, p < 0.001) impact on QoL. Consequently, H1, H2, and H3 were supported.

As for the moderating effects, the output attained from the PLS-SEM results showed that perceived emotional support failed to mitigate or dampen the adverse effects of depression on the QoL of university-disabled students as the analysis showed a path coefficient of 0.056, a t-statistic of 0.931, and a p value of 0.352 (>0.05). This result is pictured in lower Figure 2 (above right corner), which rejects H5. However, as pictured in the upper left sections of Figure 2, perceived emotional support successfully mitigates the dampening and negative effects of stress (β = 0.139, t = 2.460, p < 0.05) and anxiety (β = 0.205, t = 3.026, p < 0.01) on QoL, which supports H5 and H6.

6. Discussion

The results revealed the three factors of mental health disorders (depression, stress, and anxiety) are key determinants of QoL among KSA university-disabled students. Depression was the most effective element that highly and negatively impacted QoL (β = −0.288). This result is consistent with previous evidence, which indicated that depression could severely damage life satisfaction and functional capabilities, particularly in people who are facing extra challenges such as disability [1,55]. The high occurrence of depressive syndrome between KSA disabled students indicates the need for urgent mental health in interferences as depression arise as a key element diminishing QoL between KSA disabled students. Popular symptoms of depression include a lack of energy, decreased motivation, and a dominant mood of helplessness, which are deepened by some challenges facing disabled students in university life. For these particular students, depression can cause isolation, lower academic performance, and a low feeling of belonging. Previous research (i.e., [55]) confirmed these results, highlighting that people with disability conditions or chronic health are more prone to high depression rates, significantly and negatively impacting their overall QoL.

The results also found that stress can significantly decrease the QoL level among KSA disabled students (β = −0.147). Stress in a university context regularly comes from high academic demands, feelings of social isolation, and approachability limitations. Disabled students in KSA universities may face exceptional stressors, such as stigma, which impair their challenges [6]. The current study confirms previous research results that highlighted the negative impact of stress on well-being in similar populations [5]. To manage this issue, more efforts at the university level must design stress management program customized to the unique requirements of disabled students. These may incorporate social-psychological treatment, friends, support teams, and university policies that confirm the inclusivity and accessibility for disabled students. Applying such methods may mitigate the negative impacts of stress and significantly improve the QoL of disabled students in KSA universities.

Anxiety was another factor in our study that was found to impair QoL (β = −0.171). KSA University disabled students may encounter high levels of anxiety due to ambiguity encompassing their social and academic practices. This is in line with results from research implemented on some students with disabilities, where anxiety was associated with lower academic performance and diminished social interaction [56]. For university students with disabilities, anxiety’s effect on QoL is frequently deepened by distinctive annoyances, such as handling their situation while trying to balance academic difficulties. The two-fold burden of living with a disability and suffering from anxiety can generate a feedback loop, where emotional and physical challenges impair each other, causing a sharp decline in QoL [57]. Furthermore, social and cultural elements in KSA, such as stigma, may block university students from asking for help, further declining their QoL [58].

While the previous literature has investigated the link from mental health to QoL, limited research has been dedicated to disabled university students, specifically in the context of KSA university students. By incorporating disability with mental health disorder, and the PES as a moderator, this study improves our theoretical understanding of how psychological distress uniquely impacts the QoL of students with disabilities in KSA setting. Moreover, this study extends the implementation of the stress-buffering hypothesis introduced by Cohen and Wills [59] by showing that the PES moderates the adverse impacts of mental health disorders on QoL. The results indicated that higher emotional support weakens the detrimental impact of stress and anxiety, strengthening the theoretical assumption that emotional and social resources have a key role in psychological well-being.

The empirical findings also support the hypotheses that the PES can significantly moderate the link from mental health disorders to QoL. KSA university students who recognize and feel higher emotional support levels demonstrate a weaker adverse link from stress/anxiety to QoL, emphasizing the protective function of emotional and social resources. The PES functions as a shield against the detrimental impacts of stress. Students who perceive support from their friends, family, or university support services are better able to cope with university life stressors. This is consistent with the buffering assumption of social support, which argues that emotional support can moderate the harmful influences of stress on people’s mental health and well-being [59]. Positioning emotional support as a main investment can help disabled students to contribute in a meaningful way to society well-being, which in return can promote long-term sustainable development. For anxiety as a mental health disorder, the PES grants reassurance and a feel of security, which decreases perceptions of anxiety and successfully facilitates handling the social and academic challenges of KSA university students. Emotional support promotes a feeling of acceptance and belonging, which is specifically influential for disabled students who may encounter disregard or stigma [60]. However, our finding revealed that the PES failed to moderate the negative impact of depression on the QoL of disabled students in KSA universities. These results are interesting, taking into consideration that depression worldwide is the leading cause of disability [8]. Depression is a complicated mental health disorder categorized by a continual sense of hopelessness, sadness, and a lack of motivation. Unlike other mental health disorders (i.e., stress and anxiety), which are frequently situational and can be improved through direct emotional or social support, depression includes deep-rooted emotional and cognitive challenges that cannot be simply handled by external emotional support alone [61]. KSA disabled students often encounter extra pressures, such as limited accessibility, social stigma, and systemic restrictions, which may impair depressive signs. The chronic and inescapable nature of depression can surpass the protective impacts of the PES, as external emotional support (alone) may not be enough to manage the causal environmental and psychological elements contributing to their disorder [5]. These study results have some key practical and theoretical implications. The results highlighted the significance of incorporating mental health support services into university disability programs. Psychological intercession (i.e., cognitive-behavioral therapy) (CBT) and stress/anxiety management training can be remarkably useful for mitigating the symptoms of stress and anxiety [60]. Furthermore, promoting a comprehensive campus surrounding that fosters social interactions and psychological security is crucial. Friend support, networks, and awareness programs can act as a pivotal factor in decreasing stigma and promoting help-seeking practices. Furthermore, higher education institutions should build and develop sustainable support systems to foster retention, engagement, and lifelong outcomes for marginalized students (i.e., disabled students). The results also contribute to enhancing our theoretical understanding of how mental health disorders influence QoL in the context of disability students. By confirming the significant roles of depression, stress, and anxiety, this study highlights the importance of implementing a biopsychosocial (biological, psychological, and social factors) framework in future research on disability and well-being.

7. Limitations and Future Research Opportunities

While the results introduced some valuable implications, this study has some limitations. Using a self-reported approach with a structured questionnaire may encounter biases, which can affect the validity of the described mental health symptoms. Future research can benefit from longitudinal study designs to investigate how mental health disorders evolve over a long time and their long-term impacts on QoL. Furthermore, given the diverse characteristics of the targeted population, future research should consider using a multi-group comparison approach to evaluate the applicability of the path model across different subgroups (e.g., gender differences and various disability types). Additionally, employing a nonprobability convenience sampling technique may restrict the generalizability of our results. However, given the challenges associated with reaching and surveying students with disabilities—such as accessibility constraints and institutional procedures, convenience sampling was the most feasible method. Future research might employ other sampling methods and collect data from different contexts (institutions or cultures) to compare the results with the current results to fully understand the impact of mental health disorders on the QoL among disabled students in KSA with the PES as a moderator.

Furthermore, comparative study design can incorporate disabled students from different contexts, which may further explain the interplay between cultural elements and mental health consequences. Additionally, more moderators and mediators’ factors (physical self-esteem, religious beliefs, psychological resilience, informational or instrumental support) can be tested to understand the comprehensive relationships between mental health disorder and its potential outcomes, including QoL.