Understanding mental health disparities: a comparative study of depression and anxiety among health and non-health college track students

Abstract

Psychological problems that are common among college students across the world include depression and anxiety. The purpose of the study is to match the incidence and predictors of depression and anxiety between health and non-health students in college tracks. The cross-sectional design was adopted, and a stratified random sampling was utilized to sample 316 undergraduate students at Imam Abdulrahman bin Faisal University. The GAD-7 and PHQ-9 were used to measure the symptoms, and regression analysis was performed to discover the predictors based on the sociodemographic and psychosocial factors. The findings revealed that more than 70 percent of the respondents experienced anxiety symptoms and depression. However, no notable variation in scores according to the academic track, the study pressure, negative thinking, panic symptoms, lack of social support, poor coping, and environmental stress were found to be noteworthy predictors of the score in results of the regression models. This paper establishes the relevance of social support interventions and psychological support on the campuses of universities.

Introduction

Among the most widespread psychological issues within the circle of university students all over the world, depression and anxiety have been singled out, and there is an increasing amount of evidence demonstrating a significant increase in recent years [50, 53]. Also, changes in the daily routine, academic needs, and social status may predispose students to mental disorders since the transition to university life involves a vast number of changes (Status and Epidemiological Characteristics of Depression and Anxiety among Chinese University Students, BMC Public Health, 2025). These conditions have adverse effects on academic performance and may also impact physical health, social well-being, and the career prospects of students [1].

Differences across academic tracks

The study of variation in mental health across academic disciplines is vital, as recent research has described. The psychological stresses that students who are attending health-related programmes are subjected to often include clinical exposure, heavy course loads, and high-performance requirements [46]. Lee et al. [34] and Alkal [6] have outlined some of the stresses that students on a non-health-related track can face, such as reduced institutional support, lack of career focus, and a lack of academic engagement.

Influencing factors

Being aware and seeking assistance

The mental problem awareness within the academic realm needs to be raised to reduce stigma and boost help-seeking behaviors. According to Meda et al. [37], the psychological literacy of students should be promoted as this will allow them to recognize the first symptoms and seek immediate help to prevent the onset of more severe pathologies [47]. On this backdrop, past researchers have identified some of the factors that influence the vulnerability of students to anxiety and depression as a factor like psychosocial stressors, academic demands, coping strategies as well and cognition [58]. However, very little data has been gathered on the comparison between health and non-health academic courses, especially in non-Western contexts, even though more focus has been made on the mental well-being of international students in institutions of higher learning [59].

The limitation of the existing studies is also the lack of studies that combine psychosocial, academic, and cognitive predictors into one framework. The present study will fill these gaps by looking at:

1. Differences in depressive and anxiety symptoms in health and non-health track students.

2. The effects of sociodemographic, academic, and psychosocial variables on these symptoms.

3. What is the degree of shared (relative to discipline-related) predictors of the mental health experiences of students?

Incidence of depression and anxiety

The symptoms of major depression, or anxiety, are clinically significant and are manifested in about 30 percent of college students all over the world [36, 51]. These problems were aggravated by COVID-19, which altered the academic programmes, undermined the social relations, and created more psychological discomfort [16].

Academic pressure and mental health

Academic demands are usually associated with higher levels of psychological suffering in students. Research conducted by Chen, Liu, Ding, Ying, and Pan [17] indicates that a student who experiences continuous pressure in his/her academic performance is more likely to report more depression symptoms, especially when they use maladaptation coping strategies [54]. Similarly, Andargeery, Taani, Alhalwani and El Gazar [10] have discovered that academic stress among undergraduate students is a powerful predictor of burnout and mood disorders [24, 30].

Social support and coping strategies

Coping strategies and quality social support are paramount to the well-being of students. With intellectual and emotional support, students can manage stress and maintain psychological stability in a better way. Dong, Ge, Su, Guan, Li, Liu, Yu, Qi and Zhang [19] state that less psychological distress and better emotional conditions are also linked to such adaptive coping strategies as problem-solving, seeking assistance, and revalidating stressful events. These outcomes demonstrate the importance of creating positive campus environments and offering positive coping strategies to students (Armaghan [11, 12]).

Cognitive factors

Maladaptive cognitive patterns, such as persistent negative thinking and a pessimistic self-view, are strongly associated with the development and maintenance of mental health problems (Beck 1976; [57]). Beck's Cognitive Theory (Beck 1976) proposes that cognitive distortions and negative automatic thoughts may aggravate sadness and anxiety, particularly when students face uncertainty about their future careers or academic performance [57].

Theoretical frameworks

This study is guided by two complementary theoretical frameworks that together provide a comprehensive understanding of the psychological processes underlying depression and anxiety among university students: Lazarus and Folkman's Transactional Model of Stress and Coping [33] and Beck's Cognitive Theory (1976).

Lazarus and folkman's transactional model of stress and coping

Lazarus and Folkman's [33] transactional model posits that psychological stress is not merely a response to an external event but results from an individual's cognitive appraisal of that event and their perceived ability to cope with it [20]. The model identifies two key appraisal processes: primary appraisal, where the individual evaluates whether an event is irrelevant, benign-positive, or stressful, and secondary appraisal, where the individual assesses their resources and options for coping [52]. Coping itself is categorized into two main types: problem-focused coping, which aims to address the source of stress directly, and emotion-focused coping, which aims to regulate the emotional distress associated with the situation [25].

Application to the Present Study: This framework is directly relevant to understanding how students across different academic tracks experience and manage stress. Students in health-related tracks may appraise clinical exposure and heavy course loads as threats or challenges based on their training and perceived preparedness, while non-health students may appraise different stressors, such as career uncertainty or reduced institutional support, as particularly threatening [52]. The coping strategies students employ whether adaptive (e.g., seeking social support, problem-solving) or maladaptive (e.g., avoidance, denial) are central predictors of subsequent mental health outcomes in this study. By measuring perceived study pressure, environmental stress, and coping strategies, we operationalize key concepts from this model.

Beck’s cognitive theory

Beck's Cognitive Theory (1976) proposes that an individual's emotional responses and behaviors are largely determined by their cognitive processing of experiences. Central to this theory are negative automatic thoughts spontaneous, repetitive thoughts that are often distorted and unrealistic and cognitive distortions, which are systematic errors in reasoning, such as overgeneralization, catastrophizing, and negative self-evaluation. These maladaptive cognitive patterns are considered core features of depression and anxiety disorders, as they perpetuate negative mood states and influence how individuals interpret their experiences [49].

Application to the Present Study: This theory provides the foundation for examining the cognitive variables in our study. We directly assess negative thinking patterns and negative self-image, which are manifestations of the cognitive distortions described by Beck. According to the theory, students who exhibit high levels of negative thinking are more vulnerable to interpreting academic challenges and social interactions in a pessimistic light, thereby increasing their risk for depressive and anxiety symptoms. This framework explains why cognitive factors emerged as strong predictors in our regression models and supports the potential utility of cognitive-behavioral interventions on campus.

Integration of frameworks

Together, these two models offer a comprehensive lens for the study. Lazarus and Folkman's model explains the process by which external stressors (academic pressure, environmental stress) are appraised and managed, influencing emotional outcomes. Beck's theory explains the internal cognitive content—the negative thoughts and beliefs that mediate and moderate this process. For example, a student facing academic pressure (Lazarus) may experience negative automatic thoughts about failure or inadequacy (Beck), which in turn shapes their coping choices and ultimately their mental health. This integrated framework guides our investigation of how psychosocial factors, particularly cognitive patterns and coping resources, predict depression and anxiety among students, regardless of their academic track.

Materials and methods

Research design and study setting

The research was done at Imam Abdulrahman Bin Faisal University (IAU) in the Eastern Province of Saudi Arabia in a cross-sectional study. The university forms multiple colleges across health and non-health disciplines, which provide a suitable setting for examining differences in mental health outcomes between academic tracks.

To ensure fair and proportional representation of both academic streams, a stratified random sampling approach was used. All university colleges were first grouped into two strata:

• Health-related Colleges (e.g., Medicine, Applied Medical Sciences, Nursing, Public Health, etc.), and

• Non-health Colleges (e.g., Business Administration, Education, Computer Science, Arts).

Within each stratum, the number of students to be invited was figured out proportionately to the total enrollment in each college (N = 1,116). To preserve randomness while using an online questionnaire, student lists were first obtained through the university’s administrative system.

Student ID numbers of each stratum were then selected randomly, and only the randomly selected students were sent the survey link via official channels of communication at the university. This helped in making sure that the online format did not affect the randomness of recruitment [2].

The number of respondents who returned the questionnaire was 316, which is more than the minimum possible sample according to Stephen Thompson's formula (n = 286). This translates to a response rate of 28.3% (316/1,116), which is deemed acceptable regarding online surveys done in university institutions. What added strength to the statistical power and representativeness of the study was the higher-than-required sample size completed.

Sample size determination protocol

The sample size was calculated based on Stephen Thompson's formula, which is the right formula to apply in estimating proportions with specified confidence levels and margins of error when working with a finite population. The study sample (N) consisted of 1116 students. To obtain an approximate value of the proportion (p) with the confidence level of 95 percent (z = 1.96), an estimated proportion (p) = 0.5, and the margin of error (d) = 0.05, the formula will be as follows:

Substituting values, we obtain:

n = (1116 × 1.96² × 0.5 × 0.5)/[(0.0025 × 1115) + (1.96² × 0.25)] ≈ 286.

A total of 316 participants were recruited, exceeding the required sample size and ensuring statistical validity.

Instruments

Several standardized and contextually relevant measures were used to measure depressive symptoms, anxiety levels, and associated psychosocial variables in students. The tools were used in their proven Arabic version to make it linguistically and culturally appropriate.

Symptom assessment tools

Depression was measured in terms of item scale using the Patient Health Questionnaire PHQ-9 with nine items [5]. Each of them is rated using a 4-point Likert scale (0 to 3, respectively), where 0 means not at all and 3 means nearly every day, and the total is 0 to 27. One of them is: being uninterested or not enjoying doing things at all. The severity levels are imposed according to predetermined cutoffs: none (0–4), mild (5–9), moderate (10–14), moderately severe (15–19), and severe (20–27). The Arabic version used in this study has been established to have good internal consistency (Cronbach’s α = 0.89).

The participants were evaluated using the Generalized Anxiety Disorder Scale seven-item GAD-7 to evaluate the anxiety symptoms [5]. The rating of the questions will be on a 4-point Likert scale (ranging between 0–3) to get cumulative scores, which would have been out of 21. One of them is: nervous, nervous, or anxious. The levels of anxiety are as follows: none (0 −4), mild (5–10), moderate (11–14), severe (15–21).

The 10-item checklist assessed specific psychosocial factors as follows: perceived social support (Item 1: 'Do you believe you get emotional support when you need it?'); coping strategies (Item 2: 'Do you have specific coping mechanisms for managing stress?'); study pressure (Item 3: 'Do you experience too much studying pressure?'); negative thinking (Item 4: 'Do you often have persistent pessimistic or self-critical thoughts?'); panic symptoms (Item 5: 'Do you experience sudden episodes of intense fear or discomfort?'); emotional support quality (Item 6); self-image concerns (Item 7); environmental stress (Item 8: 'Do you experience stress related to your living situation, campus environment, or social surroundings?'); academic program motivation (Item 9); and societal view of major (Item 10).

The Arabic version of the GAD-7 has been employed in this study and has demonstrated good reliability (Cronbach’s α = 0.88).

It should be mentioned that instruments only screen the severity of the symptoms, but they do not establish a clinical diagnosis. The respondents were not clinically evaluated for depressive disorders or anxiety disorders [21].

Socio-demographic questionnaire

Demographic and academic data (age, gender, marital status, academic level, college affiliation, and field of study) were collected by means of a short self-report questionnaire [22]. The questionnaire did not include items on socioeconomic status, such as family income or parental education, nor did it assess other potentially relevant factors like living arrangements, part-time employment, or academic performance metrics.

Contributing factors checklist

The psychosocial predictors were measured using a 10-item checklist developed by the researchers specifically for this study. To minimize respondent burden and allow for a straightforward assessment of the presence or absence of key factors, each item was formatted with a binary "Yes/No" response option. The items covered three major areas:

1. Perceived social support (e.g., "Do you believe you get emotional support when you need it?")

2. Coping strategies (e.g., "Do you have specific coping mechanisms for managing stress?")

3. Academic and environmental stressors (e.g., "Do you experience too much studying pressure?"; "Do you experience environmental stress?")

Clarity and comprehension measures

To ensure that all participants, including those from non-health tracks, understood the key concepts, brief definitions were incorporated into the questionnaire preceding relevant sections [3]. The relevant items were preceded by brief explanations of terms such as panic symptoms (defined as "sudden episodes of intense fear or discomfort"), negative thinking (defined as "persistent pessimistic or self-critical thoughts"), and environmental stress (defined as "stress related to your living situation, campus environment, or social surroundings").

Validity and reliability assessment

Face validity of the checklist was reviewed by two specialists in psychiatry and educational research to ensure clarity, relevance, and applicability to the student population [28]. While this provides preliminary support for content relevance, it represents a minimal form of validity assessment. The checklist was developed specifically for this study and, as such, its content and construct validity have not been rigorously established through factor analysis or comparison with established scales. This reliance on a non-validated tool is acknowledged as a methodological limitation (see Discussion).

The internal consistency of the 10-item checklist was assessed for the current sample. The Cronbach’s alpha coefficient was 0.74, indicating acceptable reliability for exploratory research purposes. For a newly developed scale with binary items, this level of internal consistency suggests that the items moderately cohere as a measure of related psychosocial constructs, though results should be interpreted with appropriate caution.

Procedures

The study conducted an ethical and direct data gathering process. The IRB-2024–04–932 is the ethical approval of the Institutional Review Board of Imam Abdulrahman bin Faisal University. The purpose of the study, voluntariness of the research, and the time that it is anticipated to take to complete the study, and the methods of confidentiality procedures, were outlined in the electronic informed consent form, which had been sent to all the students before participation.

The data was gathered using an online self-administered questionnaire that was sent to the official communication channels of the university. The online format has been chosen because it was needed to make it accessible to different colleges and academic levels. The questionnaire allowed the participants to take their leisure time to fill in the questionnaire, and they were reminded that they could abandon the survey at any time without giving a reason.

To make it more comprehensible and minimize the possible confusion, especially among non-health students, the survey also contained concise explanations placed at the beginning of the survey before the corresponding sections. These definitions demystified some of the important terms like the signs of panic, negative thinking, environmental stress, and coping mechanisms. This proves that there was a uniform understanding of the constructions under measurement by all the participants [7, 55].

The process was done confidentially and anonymously. They did not gather any identifying information or log into the IP addresses. All the answers were stored in a database that had passwords and could only be accessed by the research team. After the study is completed, the dataset will be stored under a secure system and archived and kept, only to be used as a check, according to the guidelines of the institution [29].

Depression symptoms were measured with the help of PHQ-9 (e.g., little interest or pleasure in doing things), and anxiety symptoms were measured with the help of GAD-7 (e.g., feeling nervous, anxious, or on edge). Coping strategies, social support, and other psychosocial variables were captured with the help of the 10-item yes/no checklist that is described above [8].

Data analysis

Version 27 of IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) was used for all statistical analyses. Descriptive statistics summarized demographic characteristics and scale scores. Independent samples t-tests were conducted to compare mean depression and anxiety scores between health and non-health academic tracks.

Multiple linear regression analyses were performed to identify significant psychosocial and demographic predictors of depression and anxiety symptom severity. To assess for potential multicollinearity among the independent variables in the regression models, Variance Inflation Factor (VIF) statistics were examined. A VIF value exceeding 10 was considered indicative of problematic multicollinearity [23]. Statistical significance was set at p < 0.05 for all analyses.

Availability of data

Data to be used in this study cannot be published due to the institutional and ethical considerations that maintain the confidentiality of the participants. The respective author may provide such de-identified data in the event it is reasonable to do so; it must be provided by the Institutional Review Board.

Ethical approval

The Institutional Review Board of the Imam Abdulrahman Bin Faisal University (Approval Code: IRB-2024–04–932) gave ethical approval to conduct the given study. The ethical standards of the institutional committee were adhered to in all the research procedures, which were consistent with the principles of the Declaration of Helsinki.

Using generative AI

No generative artificial intelligence tools were used in the study design, data collection, statistical analysis, or interpretation of the results.

Results

Participant demographic and academic profile

The survey was completed by 316 undergraduate Students. Little over 50 percent of the respondents were registered in health-related courses (n = 176, 55.7%), and the other 140 students (44.3%) belonged to non-health courses. The sample consisted mostly of females (77.2%) females and most of the students were unmarried (91.8%). The sample size was balanced in terms of academic years, though none of the third-year students were included in the sample. This was due to the difference in course scheduling because third-year students on some of the programmes were not available during the collection process of data, hence they were not involved despite being in the sampling frame. The age of the participants was 18 to 28, with an average age of 21.1 years (SD = 1.9). Table 1 contains the entire description of the demographic and academic traits.

Table 1.

Demographic and academic characteristics of the participants

Variables	Categories	Frequencies	Percentages
Gender	Female	244	77.2%
	Male	72	22.8%
Academic Track	Health	176	55.7%
	Non-Health	140	44.3%
Marital Status	Single	290	91.8%
	Married	19	6.0%
	Divorced	7	2.2%

Academic Level	Number of Students	Percentage	Academic Level
First Year	40	12.7%	First Year
Second Year	84	26.6%	Second Year
Third Year	0	0.0%	Third Year
Fourth Year	90	28.5%	Fourth Year

No third-year students were included in the sample due to differences in course scheduling; third-year students in some programs were not available during the data collection period

Epidemiology of depression and anxiety symptoms

The PHQ-9 analysis showed that 73.7% of the students reported being subjected to mild to severe depressive symptoms. The most common type was mild depression (39.6%), then there was moderate (19.3%), and then moderately severe (10.4%). The proportion of people who reported severe cases of depression was 4.4 percent. Similarly, GAD-7 results showed that 71.2% of students experienced anxiety symptoms of varying severity, with moderate and severe anxiety being more than 30% of the total sample. Detailed severity distributions are shown in Tables 2 and 3.

Table 2.

Distribution of depression severity levels

Depression Level	Score Range	Percentage
No Depression	0–4	26.3%
Mild Depression	5–9	39.6%
Moderate Depression	10–14	19.3%
Moderately Severe	15–19	10.4%
Severe Depression rate	20–27	4.4%

Table 3.

Distribution of anxiety severity levels

Anxiety Level	Score Range	Percentage
Minimal Anxiety	0–4	30.7%
Mild Anxiety	5–9	37.7%
Moderate Anxiety	10–14	21.2%
Severe Anxiety	15–21	10.4%

Differences between health and non-health tracks

The t-tests that were carried out on independent samples were used to compare the results on the mean scores of depression and anxiety between the two academic tracks. The analyses showed no statistically significant variations between health and non-health students in depression scores (t = 314) = 0.45, p = 0.65) or anxiety scores (t = 314) = 0.33, p = 0.74). These results indicate that, in this sample, there was relatively similar psychological distress within the academic tracks even though the programs were different. Detailed results are presented in Table 4.

Table 4.

Independent samples t-test, depression and anxiety by academic track

Variable	Track	Mean (SD)	t-value	p-value
Depression	Health vs. non-Health	5.8 (4.3) vs. 5.6 (4.2)	0.45	0.65
Anxiety	Health vs. non-Health	6.2 (4.1) vs. 6.1 (4.0)	0.33	0.74

Depression risk factors

The psychosocial and demographic predictors of depression scoring were determined using a multiple linear regression analysis. The model was statistically significant, F (13, 302) = 15.21, p < 0.001, and explained 39.1% of the variance in the scores of depression (Adjusted R² = 0.371).

Most of the influential predictors were:

• Negative thinking (β = 0.222, p < 0.001)

• Study pressure (β = 0.170, p = 0.011)

• Panic symptoms (β = 0.115, p = 0.032)

• Environmental stress (β = 0.110, p = 0.031)

• Negative self-image (β = 0.139, p = 0.019)

• Limited coping strategies (β = –0.094, p = 0.027)

• Lack of social support (β = –0.109, p = 0.027)

Gender and academic track did not significantly contribute to the model. Complete regression coefficients are shown in Table 5

Table 5.

Multiple regression results: predictors of depression

Predictor	B	SE	Beta	t	p-Value
Constant	1.887	1.011	—	1.87	0.062
Gender (1 = Female, 2 = Male)	0.412	0.422	0.042	0.98	0.327
Track (1 = Health, 2 = non-Health)	0.361	0.387	0.038	0.93	0.352
Social Support	–1.004	0.453	–0.109	–2.22	0.027
Study Pressure	1.604	0.623	0.170	2.57	0.011
Negative Thinking	2.121	0.542	0.222	3.91	< 0.001
Panic Attacks	1.039	0.483	0.115	2.15	0.032
Coping Strategies	–0.905	0.407	–0.094	–2.22	0.027
Emotional Support	–0.568	0.442	–0.062	–1.31	0.197
Self-Image Impact	1.319	0.532	0.139	2.48	0.019
Environmental Impact	1.008	0.465	0.110	2.17	0.031
Academic Program Motivation	–0.572	0.437	–0.063	–1.31	0.191
Societal View of Major	–0.713	0.425	–0.078	–1.44	0.094

Anxiety’s predictors

A parallel regression model was used to analyze predictors of anxiety symptoms. It had a significant model, F (13, 302) = 13.27, p < 0.001, and explained 36.7% of the variance (Adjusted R 2 = 0.346).

Significant predictors of higher anxiety levels included:

• Panic symptoms (β = 0.210, p < 0.001). Panic symptoms, characterized by acute and intense fear, were included as a predictor variable due to their high comorbidity with both anxiety and depressive disorders and their potential to significantly disrupt daily functioning and academic performance [9].

• Negative thinking (β = 0.179, p = 0.003)

• Academic study pressure (β = 0.162, p = 0.013)

• Environmental stress (β = 0.104, p = 0.042)

• Lack of social support (β = –0.100, p = 0.040)

• Poor coping strategies (β = –0.096, p = 0.027)

As with depression, gender and academic track were not statistically significant predictors. Detailed results are provided in Table 6

Table 6.

Multiple regression results: predictors of anxiety

Predictor	B	SE	Beta	t	p-Value
Constant	2.489	0.961	—	2.59	0.010
Gender (1 = Female, 2 = Male)	0.163	0.410	0.016	0.40	0.691
Track (1 = Health, 2 = non-Health)	0.290	0.377	0.030	0.77	0.442
Social Support	–0.920	0.447	–0.100	–2.06	0.040
Study Pressure	1.543	0.618	0.162	2.50	0.013
Negative Thinking	1.584	0.524	0.179	3.02	0.003
Panic Attacks	1.920	0.479	0.210	4.01	< 0.001
Coping Strategies	–0.899	0.405	–0.096	–2.22	0.027
Emotional Support	–0.467	0.440	–0.051	–1.06	0.291
Self-Image Impact	0.925	0.529	0.102	1.75	0.082
Environmental Impact	0.951	0.467	0.104	2.04	0.042
Academic Program Motivation	–0.563	0.438	–0.064	–1.29	0.199
Societal View of Major	–0.612	0.425	–0.070	–1.44	0.152

Figure 1 presents the standardized beta coefficients for the regression analysis of depression and anxiety predictors. Positive and negative values reflect the direction and strength of the associations [3].

Fig. 1 .

Standardized beta coefficients (β) from multiple regression analyses predicting depression and anxiety symptom severity. Significant predictors (p <.05) are shown in color, while non-significant predictors (gender, academic track, societal view of major) are shown in gray. Negative thinking, panic symptoms, and study pressure emerged as the strongest predictors across both mental health outcomes. Error bars represent 95% confidence intervals. Significant predictors (p <.05) are visually emphasized, while non-significant predictors are de-emphasized for clarity

Multicollinearity assessment

To ensure the stability and reliability of the regression models, multicollinearity among the independent variables was assessed using Variance Inflation Factor (VIF) statistics. As shown in Table 7, all VIF values were well below the common threshold of 10, indicating that multicollinearity did not unduly influence the stability or precision of the regression coefficient estimates [23].

Table 7.

Variance Inflation Factor (VIF) values for predictor variables

Predictor	VIF (Depression Model)	VIF (Anxiety Model)
Gender	1.12	1.08
Academic Track	1.15	1.12
Social Support	1.42	1.38
Study Pressure	1.35	1.32
Negative Thinking	2.45	2.38
Panic Symptoms	1.28	1.25
Coping Strategies	1.52	1.48
Emotional Support	1.62	1.58
Self-Image Impact	2.38	2.35
Environmental Impact	1.45	1.42
Academic Program Motivation	1.32	1.30
Societal View of Major	1.28	1.26

For the depression model, VIF values for all predictors ranged from 1.12 to 2.45. For the anxiety model, VIF values ranged from 1.08 to 2.38. The highest VIF values were observed for conceptually related variables such as negative thinking and negative self-image (VIF = 2.45 and 2.38, respectively), which is expected given their theoretical overlap. However, all values remained within acceptable limits, suggesting that while some conceptual overlap exists among psychosocial variables, the statistical models remain robust and the coefficient estimates are reliable.

As shown in Fig. 1, the most significant predictors of depression were negative thinking (β = 0.21, p < 0.001), panic symptoms (β = 0.19, p < 0.001), and study pressure (β = 0.16, p < 0.01). For anxiety, the strongest predictors included negative thinking (β = 0.22, p < 0.001), panic symptoms (β = 0.20, p < 0.001), and environmental impact (β = 0.14, p < 0 0.01). Non-significant predictors (track, societal view of major) were kept in the model for completeness but visually de-emphasized in the figure.

Summary of key findings

This paper was an examination of depression and anxiety symptoms among college students taking health courses and those taking non-health courses. Over two-thirds of participants had anxiety symptoms, and moderate manifestations of anxiety were the most common, and almost three-quarters had some extent of depressive symptoms. The biggest predictors of symptom severity, according to regression analyses, were psychosocial stressors, specifically negative thinking, panic symptoms, academic pressure, low social support, few coping mechanisms, negative self-image, and environmental stress.

Demographic characteristics, such as gender and academic course, on the other hand, were not significantly predictive of anxiety or sadness. This implies that daily pressures and cognitive-emotional processes are more significant than academic specialty, and that students' psychological experiences may be more similar than different. Therefore, the study contributes to the increasing body of evidence that suggests universal psychosocial determinants may have a greater influence on student mental health than structural or program-specific factors.

Comparison with previous studies

Academic pressure

These findings confirm that academic pressure plays a major part in students' suffering. Our study shows that academic workload, deadlines, and performance expectations continue to exert significant psychological pressures, which is consistent with the findings of Yousif et al. [56], who found considerable academic stress among pharmacy students. Importantly, this trend was in both health and non-health tracks, indicating that the academic pressure is increasingly taking over in all disciplines as opposed to a field of study that is traditionally demanding as medicine.

Negative thinking and cognitive patterns

One of the best indicators of anxiety and depression was shown to be maladaptive cognitive habits. This is consistent with the metacognitive model of rumination, which emphasizes the importance of views about rumination itself: Cano-López, García-Sancho, García-Castilla and Salguero [14]. By proving that these cognitive processes function similarly across academic paths, our findings add to the body of research supporting the need for treatments that aid students in finding and changing maladaptive thought habits [4]. Students' emotional health has also been proven to be affected by contextual and environmental pressures. As an illustration of how wider environmental stressors might increase psychological vulnerability, Bathina et al. [13] saw a significant drop in population-level well-being during the COVID-19 pandemic. Students on our sample who reported more environmental stress also showed higher levels of anxiety and despair [32].

Social support

Students who reported having more supportive relationships had reduced levels of anxiety and despair, proving the sustained protective effect of social support in our sample. This tendency is supported by recent data. For instance, in a comprehensive review, Labrague [31] discovered that among people under a lot of stress, psychological resilience, adaptive coping strategies, and perceived social support are strongly associated with better mental health [31]. Similarly, Cao and Zhang [15] demonstrated that perceived social support has a major role in psychological resilience by enhancing students’ sense of competence and connectedness, which prevents the adverse impact of stress on depressive symptoms. These findings are consistent with ours and highlight the importance of improving campus-based support systems, peer networks, and mentorship relationships that all appear to be beneficial regardless of your academic career.

Coping strategies

Students more likely to report having few coping mechanisms were associated with psychological distress. This goes in line with the overall review and meta-analysis of González-Martin et al. [26], which demonstrated that coping skills and mindfulness-based therapies could effectively reduce stress and anxiety and enhance the overall mental health of university students. Also, Piet and Hougaard [41] emphasized the ability of mindfulness-based cognitive therapies to enhance resilience and emotional stability. To enhance the psychological strength and day-to-day performance of students, it is important to include training in coping skills in both extracurricular and academic life [35].

Panic symptoms

The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) characterizes panic symptoms as discrete episodes of intense fear that develop abruptly and reach a peak within minutes [9]. In the present study, panic symptoms emerged as one of the strongest predictors of anxiety severity, consistent with their central role in anxiety disorders. Although panic disorder was not the focus of this research, the significant weight of panic symptoms in the regression model underscores the importance of identifying acute anxiety symptoms early [27]. For students facing new academic and social challenges, the experience of a panic attack can be particularly destabilizing and may serve as a marker for more severe underlying distress. This finding suggests that screening for panic symptoms specifically, rather than generalized anxiety alone, may help identify students at highest risk who could benefit from targeted interventions.

Self-image and environmental stress

A study conducted by Cockshaw and Shochet [18] found negative self-image and situational stressors (including instability, exclusion, or academic anxieties) to be significant factors leading to emotional discomfort. These results confirm the relevance of the interventions aimed at the promotion of positive identities and supportive and welcoming campuses [38].

Implications of theoretical and practical

Implications for theory

The study conforms to the cognitive theory proposed by Beck and also the transaction model proposed by Lazarus and Folkman. To forecast emotional consequences, stress appraisal (e.g., academic pressure) and cognitive distortions (e.g., gloomy self-image and negative thinking) interacted, which validates the integration of environmental and cognitive mechanisms observed in both models [39]. The emerging patterns suggest potential lines of research in the future, particularly on the matter of cognitive distortions as mediators of perceived stress, although the research did not test mediation and moderation [40].

The findings provide empirical support for the integrated theoretical framework guiding this study. Consistent with Lazarus and Folkman's Transactional Model [33], academic pressure and environmental stress—both forms of primary appraisal—emerged as significant predictors of psychological distress. The protective effects of social support and coping strategies align with the model's emphasis on secondary appraisal and available resources for managing stressors.

Beck's Cognitive Theory (1976) is strongly supported by the finding that negative thinking and negative self-image were among the most powerful predictors of both depression and anxiety. These maladaptive cognitive patterns, which Beck identified as core features of emotional disorders, appear to function similarly across academic disciplines. The persistence of these cognitive predictors even when controlling for demographic and academic variables underscores their centrality in student mental health.

The interaction suggested by our integrated framework—whereby external stressors (Lazarus and Folkman) trigger negative cognitive patterns (Beck) that in turn influence emotional outcomes—represents a promising direction for future research. Longitudinal designs with mediation analyses could test whether cognitive distortions serve as mechanisms linking academic stress to mental health symptoms.

Practical implications

Curriculum Integration

Universities should consider the incorporation of stress management, coping, and mental health awareness in all academic courses to provide full support.

Peer supports and mentoring programmes

Support the social networks to help pupils avoid the stressors of the environment and minimize the occurrence of anxiety and depressive symptoms [42].

Focused cognitive interventions

To reduce the impact of maladaptive cognitive processes, hold psychoeducational classes, which will focus on negative thinking and self-image [43].

Online and blended solutions

To support students who may not want to give face-to-face counseling, provide more access to apps to monitor stress, do breathing exercises, and practice mindfulness [44].

Universal approaches

Mental health therapies should be made available to all students, and not just those in some faculties, since academic track was not found to be a significant predictor [45].

Culturally-adapted recommendations for Saudi Universities

Based on our findings and the cultural context of Saudi higher education, we recommend the following actionable strategies:

1. Faith-Integrated Psychoeducation: Develop mental health awareness programs that incorporate Islamic principles of emotional well-being, reliance on social support (silat al-rahim), and the concept of tawakkul (trust in God) as adaptive coping strategies. This culturally congruent approach may reduce stigma and increase help-seeking.

2. Gender-Sensitive Support Services: Given the sex-segregated nature of Saudi campuses, establish parallel mental health services in both male and female sections, with providers trained in culturally appropriate communication.

3. Family Engagement Initiatives: Create family-oriented mental health workshops during orientation periods, recognizing the central role of family in Saudi students' lives and support systems.

4. Arabic Digital Tools: Collaborate with university technology units to develop Arabic-language mental health apps that include mindfulness exercises, panic symptom management protocols, and cognitive restructuring exercises adapted for local cultural contexts.

5. Peer Support Networks: Train students as mental health ambassadors who can provide initial support and connect peers with professional services, respecting cultural norms around help-seeking within same-gender groups."

Future research directions and limitations

While this study provides valuable insights into the mental health of university students, several limitations must be acknowledged when interpreting the findings.

First, the cross-sectional design precludes any determination of causal relationships between psychosocial factors and mental health outcomes [48]. Although we identified significant associations between predictors such as negative thinking and depression scores, we cannot establish whether these cognitive patterns precede and contribute to depressive symptoms or whether they are consequences of existing depression. Longitudinal or cohort designs are needed to investigate temporal sequences and identify causal pathways [60].

Second, the reliance on self-report measures introduces the potential for recall bias and social desirability bias. While the PHQ-9 and GAD-7 are well-validated screening instruments, they do not substitute for clinical diagnostic interviews. Future studies would benefit from incorporating clinical interviews, ecological momentary assessment, or objective behavioral monitoring (e.g., wearable devices to track sleep patterns or physiological stress indicators) to enhance measurement accuracy.

Third, and most notably, the study assessed a limited range of potentially influential variables. The 10-item checklist used to measure psychosocial predictors was developed specifically for this study and, as a non-validated tool with binary response options, may not capture the full complexity of constructs such as coping, social support, and cognitive patterns. Furthermore, several important contextual factors were not assessed, including:

• Socioeconomic background (family income, parental education, financial stress)

• Living arrangements (on-campus vs. off-campus, living with family vs. independently)

• Academic performance metrics (GPA, course grades, academic probation status)

• Physical health factors (chronic illnesses, sleep quality, exercise habits)

The absence of socioeconomic data is particularly significant given that financial stress and family background are well-established determinants of mental health outcomes in student populations [4]. Future research should incorporate these variables to build more comprehensive predictive models.

Fourth, the measurement properties of the psychosocial checklist warrant caution. While face validity was established through expert review, the internal consistency of the 10-item checklist was assessed for the current sample, yielding a Cronbach’s alpha coefficient of 0.74, indicating acceptable reliability for exploratory research purposes. For a newly developed scale with binary items, this level of internal consistency suggests that the items moderately cohere as a measure of related psychosocial constructs, though results should be interpreted with appropriate caution.

Fifth, while we assessed multicollinearity and confirmed that it did not compromise the regression models, the conceptual overlap among cognitive predictors (e.g., negative thinking and negative self-image) raises questions about their distinct contributions. Future research with larger samples could employ structural equation modeling or factor analysis to better disentangle these related constructs.

Sixth, the study did not examine interaction effects, mediation, or moderation among the psychosocial variables. For example, coping strategies may buffer the impact of academic stress on anxiety, or negative thinking may mediate the relationship between environmental stress and depression. Although the primary aim was to identify significant predictors, more sophisticated analytical approaches could reveal the dynamic processes underlying student mental health.

Seventh, while we compared health and non-health tracks broadly, we did not examine discipline-specific differences within each track. Students in high-intensity programs such as engineering, architecture, or nursing may experience unique stressors not captured by the health/non-health dichotomy. Future research should examine program-specific variations to inform tailored interventions.

The complete absence of third-year students from our sample represents a notable limitation, as this cohort often faces unique stressors including increased clinical exposure (in health tracks), major-specific coursework (in all disciplines), and career preparation pressures. The mental health experiences of third-year students may differ substantially from those in other academic years. Future research should ensure representation across all academic levels to examine potential developmental trajectories in psychological distress during the university journey.

Finally, the absence of third-year students from the sample, due to scheduling constraints during data collection, limits the generalizability of findings across all academic years. The mental health experiences of third-year students, who often face increased clinical or major-specific demands, may differ from those of first-, second-, and fourth-year students.

The binary Yes/No format of the psychosocial checklist, while minimizing respondent burden, likely oversimplifies nuanced psychological constructs such as coping efficacy and social support quality. Future studies should employ validated multi-item scales (e.g., Multidimensional Scale of Perceived Social Support, Brief COPE) to capture the full spectrum of these variables. Additionally, the checklist's content validity was established only through expert review; comprehensive validation through confirmatory factor analysis and test–retest reliability assessment would strengthen confidence in its measurement properties.

Despite these limitations, the study provides important preliminary evidence regarding the psychosocial factors associated with depression and anxiety among university students and offers directions for future research and intervention development.

Conclusions

This study indicates that approximately three-quarters of students in universities demonstrate symptoms of depression and anxiety of clinical relevance. The findings point out that the psychosocial characteristics are above the demographic variables, such as gender or academic track, in particular, negative thinking patterns, academic pressure, panic symptoms, and reduced social support and coping skills.

The research contributes to the understanding of psychological stresses that determine the well-being of the students, as it not only analyzes health and non-health academic tracks simultaneously but also incorporates a vast number of psychosocial variables. These results point to the importance of comprehensive mental health interventions that integrate specific support of pupils with heightened cognitive or emotional vulnerability and general prevention.

Longitudinal designs and the incorporation of more contextual and behavioral variables would be beneficial for future studies. In addition to helping universities create evidence-based therapies that address the changing mental health needs of different student populations, these efforts will help elucidate the dynamic interaction between stress, cognition, and environmental factors.

Abbreviations

GAD-7

Generalized Anxiety Disorder - 7

CBT

Cognitive Behavioural Therapy

IRB

Institutional Review Board

PHQ-9

Patient Health Questionnaire - 9

VIF

Variance Inflation Factor

DSM-5

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

SPSS

Statistical Package for the Social Sciences

Authors’ contributions

NMK and AA conceptualized and designed the study. AAH, SRA, and FA-D were involved in data collection. BMA and FA-D performed the statistical analysis. NMK and AA drafted the initial manuscript. All authors (NMK, AA, AAH, SRA, FA-D, BMA) contributed to the interpretation of data, critically revised the manuscript for important intellectual content, and read and approved the final manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

Data to be used in this study cannot be published due to the institutional and ethical considerations that maintain the confidentiality of the participants. The respective author may provide such de-identified data in the event it is reasonable to do so; it must be provided by the Institutional Review Board.

Declarations

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Imam Abdulrahman Bin Faisal University (Approval Code: IRB-2024–04-932). All procedures performed were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki declaration and its later amendments. Electronic informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable. This manuscript does not contain any individual person's data in any form (including individual details, images, or videos).

Competing interests

The authors declare no competing interests.