Editorial Board

A Refereed Monthly International Journal of Management

A Study on Cyber Victimization and its Impact on Mental Health

 

Dipen Satyal

B.Sc. in Statistics,

Tribhuvan University, Nepal

dipinsatyal2001@gmail.com

 

Aayushma Pyakurel

B.Sc. in Statistics,

Tribhuvan University, Nepal

iushmapyakurel53@gmail.com

 

Bijay Lal Pradhan

PhD Associate Professor,

Tribhuvan University, Nepal

bijaya.pradhan@ac.tu.edu.np

Corresponding Author

ORCID id: 0000-0001-5673-4415

 

Prof. Hemant Kothari

President,

Pacific Academy of Higher Education

and Research University, Udaipur,

Rajasthan, India

Corresponding Author

 

Nabaraj Poudyal,

Assistant Professor,

Tribhuvan University, Nepal

nrpl2001@gmail.com

 

Abstract:

The rapid expansion of digital communication and social networking platforms has increased opportunities for interaction but has also intensified the risk of cyber victimization, a pervasive form of online abuse linked to serious psychological consequences. This study investigates the relationship between cyber victimization and mental health among Nepalese internet users and examines the mediating role of coping strategies. Using a cross-sectional analytical design, data were collected from 389 participants aged 13–63 years via an online survey. Standardized instruments including the Cyber Victimization Scale, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Coping Strategies Scale were employed. Data analysis was performed in R, applying descriptive statistics, reliability and validity testing, ANOVA, Chi-square tests, and Structural Equation Modeling (SEM).

Results indicated satisfactory reliability and validity for all scales (Cronbach’s α > 0.70, AVE > 0.5). Cyber victimization was significantly associated with increased use of coping strategies (β = 0.17, p < .01); however, coping did not significantly mediate the relationship between cyber victimization and mental health outcomes. Depression and anxiety were found to be influenced primarily by demographic variables, females and older adults (26 years and above) reported higher psychological distress. Education showed a modest effect on depression, while profession had no significant influence.

These findings highlight that while cyber victimization is prevalent across demographic groups, its psychological impact varies by age and gender. Adaptive coping strategies appear widely used but insufficient in mitigating distress. The study underscores the urgent need for culturally tailored digital safety programs and gender-sensitive mental health interventions in Nepal’s growing online community.

Keywords: Cyber victimization, mental health, coping strategies, depression, anxiety, Nepal, Structural Equation Modeling (SEM)

 

Introduction:

The emergence of social networking sites and digital technologies has transformed the ways of communication, education, and socializing of people. With these opportunities, however, there is a darker side the world that is termed cyber victimization. In contrast to traditional bullying, the online one becomes chronic and can be very anonymous, and is potentially able to reach large audiences, which leaves the victim with permanent psychological scars. Evidences from the cross country studies shows that, there are steady correlates between cyber victimization and depression, anxiety, low self-esteem, social withdrawal, and suicidal ideation (Galderisi, Heinz, Kastrup, Beezhold, & Sartorius, 2015; Musharraf & Anis-ul-Haque, 2018; Wright, 2016).

Maurya, Muhammad, Dhillon, and Maurya (2022) studied Indian teenagers and young adults and found that being bullied online made their mental health much worse. After three years, those who faced ongoing harassment showed a sharp increase in depression and suicidal thoughts. Likewise, Bonanno and Hymel (2013) have observed that cyberbullying has its psychological consequences that hold as high even when offline bullying is controlled. Moreover, other researchers like Makarova and Makarova (2023) and Bottino, Bottino, Regina, Correia, and Ribeiro (2015) confirmed that the effects of online abuse cause emotional distress, which is frequently experienced in a physical form like sleep disturbance, headaches, and other psychosomatic reactions.

Simultaneously, cultural and social forces determine the effects of cyber victimization on people. Research conducted in Pakistan showed that female students are especially susceptible to the rise of anxiety and depression as a result of online bullying (Ahmmad, Iqbal, & Naz, 2024; Musharraf & Anis-ul-Haque, 2018). Beyond these vulnerabilities, the ways individuals cope with cyber harassment significantly shape its psychological impact. Study by Eroglu, Peker, and Cengiz (2022) shows that maladaptive coping strategies, such as avoidance or rumination, can intensify distress, while adaptive coping strategies, including seeking social support or problem-solving, can buffer against the harmful effects of cyber victimization.

Although a body of evidence has been developed, there is a research gap still clear in Nepal. The number of studies based on Western countries and South of Asia countries such as India and Pakistan has been numerous and hardly any empirical studies have explored the impact of online victimization on mental health in the Nepalese context. Also, there is limited research in South Asia that has used the standardized psychological instruments like PHQ-9 to test depression and GAD-7 to test anxiety, and there are even fewer studies that have used the more sophisticated method like Structural Equation Modeling (SEM) to establish the mediation impact. This lack of local data does not allow creating culturally belonging interventions, awareness campaigns and digital safety policies specific to the ever growing Nepal online population.

These gaps that included the effects of cyber victimization on mental health are comprised in the present study through exploring the experience of 389 Nepalese study participants (aged between 13 and 63 years). Using validated measure instruments (PHQ-9, GAD-7), integrating the coping strategy as an intervening variable into the framework of a SEM model, the study can not only quantify the psychological burden of online abuse, but also ascertain the mechanisms by which it achieves its effects. By so doing, it will add context-driven evidence to the body of global scholarship, but it will also provide insights that teachers, policymakers and mental health professionals in Nepal can put into practice.

Methodology:

Cyber victimization and outcome variables were assessed using a cross-sectional analytical design that tested the associations between cyber victimization and mental health in the context of Nepal. The information was gathered using an online survey, which is commonly applied to investigate psychosocial and behavioral trends when in a digital environment as by (Maurya et al., 2022; Musharraf & Anis-ul-Haque, 2018).

The subjects that were part of the study were Nepalese internet users who were between 13 and 65 years and were literate in English. A purposive non-probability sampling method was used where the participants were recruited through the survey links shared on social media such as Facebook, WhatsApp, Instagram, Viber, and Messenger similar to Kothari and lal Pradhan (2012). Such strategy was suitable in addressing the digitally active populations, which form a higher risk of falling victims of cyber-attacks as seen in Stevens, Nurse, and Arief (2021).

Data were collected using a structured, self-administered questionnaire developed in Google Forms. The tool comprised four sections:

• Socio-demographic details (age, gender, education, occupation).
• Cyber Victimization Scale, assessing frequency and types of online abuse on a 5-point Likert scale.
• Mental health screening using the Patient Health Questionnaire-9 (PHQ-9) for depression and Generalized Anxiety Disorder-7 (GAD-7) for anxiety both validated and widely used in global and South Asian contexts.
• Coping Strategies Scale, adapted from established frameworks of stress and coping.

These instruments were chosen for their demonstrated reliability and applicability in assessing psychological outcomes associated with digital victimization.

Measurement and Variables

• Independent variables: cyber victimization.
• Dependent variables: depression (PHQ-9) and anxiety (GAD-7).
• Mediating variable: coping strategies.

Standard scoring cut-offs were applied: PHQ-9 classified depression into none, mild, moderate, moderately severe, and severe categories; GAD-7 classified anxiety as mild, moderate, and severe.

 

 

 

Components	Variables	References	Notation
Socio-demographic	age, gender, education, occupation	(Alrajeh, Hassan, Al-Ahmed, & Alsayed Hassan, 2021)
Cyber-victimization	Cyber-victimization	(Kara & Aslan, 2025)	CV
Mental Health	Anxiety and Depression	(Audrin & Blaya, 2020)	PHQ & GAD
Coping Strategy	Coping strategy	(Eroglu et al., 2022)	CS

 

 

Figure 1: Conceptual Framework

Data were exported to CSV, and analyzed in R. The preliminary cleaning included removal of duplicate entries of data, management of NA values and recoding variables. The analysis involved a three-step process that was as follows:

• Demographic and study variables were summarized using descriptive statistics like in (Alrajeh et al., 2021; Rahman, Hossain, Bristy, Hoque, & Hossain, 2023; Shrestha et al., 2024).
• To assess the internal consistency of reliability similar to Pokharel, Pradhan, and Pradhan (2023), Cronbach Alpha, Composite Reliability (rho c) and to access validity Average Variance Extracted (AVE) were identified as seen in (Pradhan, Kothari, & Chalise, 2023). The reliability was satisfactory, against all scales (0.70 > 0.00), similar to previous cross-cultural data (Bonanno & Hymel, 2013; Kara & Aslan, 2025) and AVE was also satisfactory for each scale.
• Inferential statistics: Categorical associations were examined by Chi-square test with Monte Carlo Simulation as well as MANOVA used to determine the overall effects of demographic factors on outcomes of psychology respective to (Galderisi et al., 2015; Sidera, Serrat, & Rostan, 2021; Song, Zhou, Niu, Fan, & Zhou, 2024) followed by the post hoc analysis.

Structural Equation Modeling (SEM) similar in PATHAK, KADYAN, KOTHARI, SINGH, and SRINIVAS (2025) was carried out to assess both direct and mediated effects of information to assess cyber victimizations on mental health outcomes via coping strategies. Structural Equation Modeling (SEM) serves as a robust analytical framework for examining complex psychosocial relationships, as extensively demonstrated in prior literature (Audrin & Blaya, 2020; Hans, Rana, & Narula, 2025; Kara & Aslan, 2025).

The questionnaire was answered by respondents after they gave electronic informed consent, and no information that would allow the identification of the respondent was obtained.

Findings:

In this study, the demographic features of the participants are important because they bring together varied opinions on various genders, ages, education, and experiences, thus making the study to have a comprehensive grasp of the contributing factors to cyber victimization and the associated effect on the psychological well-being of the participants.

Table 1: Demographic

Variable		Classification		Frequency
Gender	Male Female		153       236
Age Group		13-19		74
		20-25 26-35 36+		244 60 11
Profession		IT Non-IT Student/Unemployed Others		38 60 272 19

 

 

 

 

 

 

Table 2: Descriptive Statistics

Variable	N		Min	Max	Mean	SD
Cyber Victimization		389	6	24	11.9	4.54
Depression	389		9	36	16.6	6.07
Anxiety	389		7	28	13.7	5.55
Copping Strategy	389		8	40	31.2	9.16

 

The observed mean score of 11.9 falls below the scale midpoint of 15 (on a range of 6–24). While this indicates that all participants experienced some degree of cyber victimization, given that the minimum possible score is 6, the overall intensity or frequency of such experiences appears to be relatively low within the sample.

The participants had expected and reported lower levels of depressive symptoms, with a mean score of 16.6, which is less than the middle point 22.5(on a range of 9-36). This implies that, in spite of the fact that some symptoms of depression were evident, they were mostly mild to moderate, but not serious.

The average anxiety was 13.7 which is lower than the mid-point of the scale 17.5. Just like in depression, this means that the symptoms of anxiety were exhibited but were typically moderate, which means that there was a mild to moderate degree of emotional distress in the sample.

Interestingly, the mean coping strategy score was 31.2, exceeding the scale midpoint of 24 (range: 8–40). This suggests that participants were actively employing coping mechanisms, which may serve a protective role in mitigating the psychological impact of cyber victimization.

Notably, the above-average score in coping strategy suggests that the participants have adaptive coping strategies. Here, there are possible strategies that can assist in cushioning the negative consequences of cyber victimization and reduce the intensity of related mental health factors. This observation highlights the possible importance of improving coping abilities in young people as a preventive against mental health problems in cyberspace.

 

Table 3: Measurement of validity and reliability

Construct	Items		Loading	Cronbach’s Alpha	rohC	AVE	rhoA
Cyber Victimization             Coping Strategy                 Depression (PHQ-9)                 Anxiety (GAD-7)		CV1 CV2 CV3 CV4 CV5 CV6   CS1 CS2 CS3 CS4 CS5 CS6 CS7 CS8   PHQ1 PHQ2 PHQ3 PHQ4 PHQ5 PHQ6 PHQ7 PHQ8 PHQ9   GAD1 GAD2 GAD3 GAD4 GAD5 GAD6 GAD7	0.65 0.72 0.81 0.68 0.75 0.74   0.89 0.9 0.82 0.84 0.91 0.89 0.88 0.86   0.62 0.72 0.69 0.7 0.74 0.74 0.72 0.75 0.66   0.77 0.82 0.81 0.79 0.76 0.76 0.8	0.867               0.963                 0.897                   0.919	0.868                      0.963                       0.899                       0.920	0.525               0.765                 0.504                   0.623	0.868               0.963                 0.899                   0.920

 

 

 

 

Table 2 shows the validity and reliability of the measures of cyber-victimization, copping strategy, and mental health (Anxiety & Depression), all constructs demonstrate strong reliability, with Cronbach’s Alpha values above 0.7, indicating good internal consistency. The composite reliability (rhoC) for each construct is also high, surpassing 0.7, indicating strong construct reliability. The Average Variance Extracted (AVE) for each construct is above the 0.5 threshold, confirming good convergent validity. Overall, the constructs show robust reliability and validity, supporting the model's effectiveness.

 

 

Figure 2: Structural Equation Modeling

A mediation analysis was conducted using SEM to examine whether coping strategies (CS) mediate the relationship between cyber victimization (CV) and mental health (MH), with MH operationalized as a second-order factor composed of depression (PHQ) and anxiety (GAD). Results revealed a significant positive association between CV and CS (β = 0.17, t = 3.03, p < .01). However, the effects of CS on MH (β = 0.05, t = 0.63, ns) and CV on MH (β = 0.15, t = 1.11, ns) were not significant. The indirect effect of CV on MH through CS was small and non-significant (β = 0.0085). These findings suggest that coping strategies do not mediate the relationship between cyber victimization variables and mental health, although cyber victimization significantly predict greater use of coping strategies. Mental health was significantly associated with anxiety symptoms (β = 0.85, t = 2.84, p < .01), but not with depressive symptoms.

The findings highlight that while individuals exposed to cyber victimization tend to engage more in coping behaviors, these strategies may not be sufficient to improve overall mental health. Mental health remains closely associated with anxiety outcomes, underscoring the need for more effective coping interventions.

Table 4: Chi-Square Test Results

Tests	Chi-square(χ2)	df	p-value	Interpretation
CV×AGE	11.21	8	0.1820	Insignificant
CS×AGE	6.90	12	0.8725	Insignificant
GENDER×CV	1.38	4	0.9086	Insignificant
GENDER×CS	2.26	6	0.7759	Insignificant
GENDER×PHQ	13.94	8	0.1350	Insignificant
GENDER×GAD	10.78	6	0.0993	Insignificant

 

Analyses were performed to examine the relationships between cyber victimization (CV), coping strategies (CS), psychological symptoms, and demographic variables. The Chi-square results indicated that none of the relationships between cyber victimization, coping strategies, and demographic variables (age and gender) were statistically significant. Similarly, no significant associations were observed between gender and psychological symptoms (depression and anxiety). These findings suggest that age and gender do not have a significant influence on cyber victimization, coping strategies, or psychological symptoms within this sample.

 

 

 

 

 

Table 5: Univariate ANOVA Results for CV, CS, PHQ, and GAD by Profession

Dependent Variable	Independent variable df		Residual df	F-value	p-value	Significance
Cyber-victimization		14	370	0.997	0.455
Coping Strategy	14		374	1.384	0.158
Depression(PHQ)	14		374	1.256	0.232
Anxiety(GAD)	14		374	0.775	0.697

The univariate analyses indicated that Profession did not have a meaningful impact on any of these variables. That is, individuals from different professional backgrounds did not differ significantly in their levels of cyber-victimization, coping strategies, anxiety, or depression. These results suggest that profession alone is not a determining factor in influencing these psychological and behavioral outcomes.

Table 6: Univariate ANOVA Results for CV, CS, PHQ, and GAD by Age Group

Dependent Variable	Independent variable df		Residual df	F-value	p-value	Significance
Cyber-victimization		4	380	2.168	0.072
Coping Strategy	4		384	0.424	0.791
Depression(PHQ)	4		384	3.766	0.005	**
Anxiety(GAD)	4		384	2.883	0.022	*

The univariate analysis showed that Age Group established a substantial link with anxiety and depression which demonstrated that people of various age groups experienced different degrees of psychological distress. The way people experience anxiety and depression symptoms seems to change as they get older. The study showed that age group had no effect on cyber-victimization or coping strategies which indicates these behaviors stay stable throughout different age groups.

Table 7: POST-HOC Summary of Age Group

Dependent	Comparison	Mean-Diff	CI-Lower	CI-Upper	p-value	Sig
Depression (PHQ)	26-35vs13-19	-2.73	-5.40	-0.068	0.041	*
Depression (PHQ)	36+vs13-19	-5.97	-10.94	-1.008	0.010	*
Depression (PHQ)	26-35vs20-25	-2.55	-4.76	-0.33	0.016	*
Depression (PHQ)	36+vs20-25	-5.78	-10.52	-1.051	0.009	**
Anxiety (GAD)	36+vs13-19	-4.94	-9.52	-0.36	0.028	*
Anxiety (GAD)	36+vs20-25	-4.73	-9.09	-0.36	0.027	*

Post hoc Tukey HSD comparisons revealed significant age-related differences in both depression and anxiety scores.

For depression (PHQ), participants aged 26–35 years reported significantly higher depressive symptoms than those aged 13–19 years (Mean Diff = −2.73, p = 0.041), and those aged 36 years and above exhibited even greater depressive symptoms than both the 13–19 years (Mean Diff = −5.97, p = 0.010) and 20–25 years groups (Mean Diff = −5.78, p = 0.009). Additionally, the 26–35 years group also reported higher depression levels than the 20–25 years group (Mean Diff = −2.55, p = 0.016). These findings suggest a progressive increase in depressive symptomatology with advancing age, particularly noticeable among adults over 25 years.

Regarding anxiety (GAD), a similar pattern emerged. Participants aged 36 years and above showed significantly higher anxiety levels compared with both the 13–19 years (Mean Diff = −4.94, p = 0.028) and 20–25 years (Mean Diff = −4.73, p = 0.027) groups. This pattern indicates that older participants may experience heightened emotional distress relative to adolescents and young adults.

Overall, these post hoc results underscore a clear age gradient in psychological distress, with older participants reporting greater levels of both depressive and anxiety symptoms. This trend may reflect increasing life stressors, cumulative exposure to psychosocial challenges, or differential coping capacities across developmental stages. The findings emphasize the importance of age-tailored mental health interventions, particularly targeting mid- and late-adulthood populations who may be more vulnerable to mood and anxiety disturbances.

Table 8: Univariate ANOVA Results for CV, CS, PHQ, and GAD by Gender

Dependent Variable	Independent variable df	Residual df	F-value	p-value	Significance
Cyber-victimization	2	382	0.469	0.626
Coping Strategy	2	386	0.575	0.563
Depression(PHQ)	2	386	5.340	0.005	**
Anxiety(GAD)	2	386	4.482	0.012	*

The univariate analyses showed that gender creates a major impact on anxiety and depression which means males and females experience psychological distress in distinct ways. Research indicates that gender affects depressive and anxiety symptoms which suggests men and women experience emotional risks and handle distress in various ways.

The study shows that online harassment affects all genders equally because people from different genders face the same level of cyber-victimization and show similar patterns of coping strategy usage.

Table 9: POST-HOC Summary of Gender

Dependent	Comparison	Mean Diff	CI Lower	CI Upper	P value	Significance
Depression (PHQ)	Male-Female	-2.30	-3.76	-0.84	0.0006	***
Anxiety (GAD)	Male-Female	-1.95	-3.29	-0.617	0.001	**

The post hoc Tukey HSD tests revealed statistically significant gender differences in both depression (PHQ) and anxiety (GAD) scores. Specifically, males reported significantly lower depression scores compared to females (Mean Difference = −2.30, 95% CI [−3.76, −0.84], p = 0.0006), indicating that females experienced higher levels of depressive symptoms. Similarly, males also exhibited significantly lower anxiety levels than females (mean difference = −1.95, 95% CI [−3.29, −0.62], p = 0.001).

These findings are consistent with existing literature Alrajeh et al. (2021) suggesting that females are more prone to experiencing depression and anxiety, possibly due to gendered psychosocial stressors, emotional expressivity, and social expectations. The observed differences highlight the need for gender-sensitive mental health interventions and awareness programs, particularly those addressing the unique emotional and social challenges faced by women.

Table 10: Univariate ANOVA Results for CV, CS, PHQ, and GAD by Education

Dependent Variable	Independent variable df		Residual df	F-value	p-value	Significance
Cyber-victimization		4	380	2.327	0.056
Coping Strategy	4		384	1.018	0.398
Depression(PHQ)	4		384	2.700	0.030	*
Anxiety(GAD)	4		384	4.482	0.574

The univariate analyses revealed that Education created a significant impact on depressive symptoms (PHQ) which shows that people with various educational backgrounds experience different levels of depression. The findings indicate that education levels might influence how people develop mental toughness against stress or their susceptibility to mental health problems.

The results showed that Education did not produce any meaningful changes in cyber-victimization or coping strategies or anxiety (GAD) which shows these behavioral and emotional responses stay uniform across different educational backgrounds.

Univariate analysis showed that Age and Gender produced important changes in anxiety and depression levels yet Education played a role in shaping depression symptoms. The study showed that Profession did not affect the results but all demographic groups showed similar patterns of cyber-victimization and coping strategies. The research indicates that mental health results show greater sensitivity to demographic elements than to behavioral changes.

Table 11: Post Hoc summary of Education

Dependent	Comparison	Mean-Diff	CI-Lower	CI-Upper	p-value	Sig
Depression (PHQ)	Master's level-Bachelor's level	-2.49	-5.02	0.03	0.05	.

 

Post hoc analyses were conducted to examine pairwise differences in depression (PHQ) and anxiety (GAD) scores across education levels. For depression, no comparisons reached conventional statistical significance, although participants with a Master’s degree showed a trend toward slightly lower PHQ scores compared with those holding a Bachelor’s degree (Mean difference = -2.49, 95% CI: -5.02 to 0.03, p = 0.05).

 

Discussion and Conclusion:

The research study analyzed how demographic elements which include Gender and Age Group and Profession and Education affect cyber-victimization (CV), coping strategies (CS), depression (PHQ), and anxiety (GAD). The Chi-square analysis showed few important relationships between the categorical variables similar to (Sidera et al., 2021). The study found a connection between CV and CS which suggests that people who face cyber-victimization might use different coping methods. The study used other Chi-square tests to analyze Gender against CV and CS and PHQ and GAD but these tests showed no significant results which indicates that categorical relationships do not fully explain the changes in these results.

The present study examined the influence of key demographic factors, Profession, Age, Gender, and Education, on cyber-victimization, coping strategies, and psychological outcomes (depression and anxiety). Univariate ANOVA results indicated that Profession did not significantly affect any of the outcomes, suggesting that occupational background alone does not determine vulnerability to online victimization or psychological distress similar to Palermiti, Bartolo, Musso, Servidio, and Costabile (2022) and Cassidy, Faucher, and Jackson (2018). In contrast, Age and Gender emerged as significant predictors of depression and anxiety. Post hoc analyses revealed a clear age gradient, with older participants (26–35 and 36+ years) reporting higher depressive and anxiety symptoms compared to younger groups (13–25 years), indicating that psychological distress may increase with age, possibly due to accumulated life stressors or changes in coping capacity as mentioned on (Farooqui, 2021; Musharraf & Anis-ul-Haque, 2018). Gender differences were also evident, with females exhibiting significantly higher levels of both depression and anxiety than males, consistent with prior research highlighting gendered susceptibility to emotional stressors (Alrajeh et al., 2021; Baldasare, Bauman, Goldman, & Robie, 2012).

Education showed a significant effect on depression scores in the ANOVA; however, pairwise post hoc comparisons were minimally statistically significant, suggesting that educational level may influence depressive symptoms in a general, diffuse manner rather than through large differences between specific categories, can also be seen on Baldasare et al. (2012). Notably, neither education nor the other demographic factors had a meaningful impact on cyber-victimization or coping strategies, indicating that these behaviors may be relatively stable across different demographic groups (Cassidy et al., 2018; Olweus & Limber, 2018). Overall, these findings underscore the predominant role of Age and Gender in shaping psychological outcomes, while educational and professional factors exert a more modest or diffuse influence, highlighting the need for targeted mental health interventions that consider demographic vulnerabilities.

The research findings demonstrate that demographic elements create different effects on behavioral results and psychological results. Studies show that cyber-victimization rates remain constant across different demographic groups yet the way people handle cyber-victimization stays consistent across groups also found on (Alrajeh et al., 2021). The research indicates that anxiety and depression levels change based on Gender, Age and Education. Targeted mental health programs need to address these elements but preventive cyber-victimization strategies should implement universal methods which avoid singling out particular demographic groups. The research shows that coping strategies function as a link between cyber-victimization and psychological outcomes which suggests new intervention possibilities as similar to (Audrin & Blaya, 2020).

The research shows different demographic elements produce various effects on behavioral and psychological results which occur during cyber experiences. The research study identified that Gender and Age functions as main risk elements for anxiety and depression yet education shows a protective effect against depression. The study results showed that professional status did not create any differences in outcomes and every group showed similar behavioral patterns regarding cyber-victimization and coping strategies. The research results demonstrate that mental health programs need specific adaptation for at-risk populations but they should continue to protect all people from cyber-victimization. Future studies need to examine multiple psychosocial and environmental elements together with possible mediating or moderating factors to understand better how demographic variables affect behavioral responses and mental health results.

 

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