Borderline Personality Disorder Symptoms in a Conflict-Affected Population: A Study Among University Students in a Low-Middle-Income Country

Borderline Personality Disorder Symptoms in a Conflict-Affected Population: A Study Among University Students in a Low-Middle-Income Country | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Borderline Personality Disorder Symptoms in a Conflict-Affected Population: A Study Among University Students in a Low-Middle-Income Country Ahmad A. Hanani, Merna Al-Rashayda, Asalah Shhadi, Tala Karaka, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6746037/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: Borderline Personality Disorder (BPD) is a severe mental health condition that is often intensified by trauma, instability, and sociocultural stressors. This study investigates the prevalence and psychological predictors of BPD symptoms among Palestinian university students in a politically unstable context. Methods: A cross-sectional study was conducted among 538 participants across the West Bank using validated Arabic versions of the McLean Screening Instrument and the Borderline Personality Questionnaire. Statistical analyses included Spearman’s correlation, Mann-Whitney, Kruskal-Wallis, linear regression, and path analysis. Results: Females reported significantly higher levels of affective instability, abandonment fears, and intense anger. Exposure to sociopolitical violence (e.g., living near settlements or being attacked) was associated with elevated BPD symptoms. Regression and path analysis identified disturbed self-image, abandonment, impulsivity, and quasi-psychotic symptoms as key predictors of self-harm. Unexpectedly, transportation methods and medication use were also associated with symptom severity. Conclusion: Sociopolitical adversity, gender, and psychological vulnerabilities significantly influence BPD symptom expression in Palestinian youth. These findings highlight the urgent need for culturally tailored, trauma-informed, and gender-sensitive mental health interventions in conflict zones. Future longitudinal studies are recommended to monitor symptom development and evaluate the effectiveness of targeted therapies. Borderline Personality Disorder Sociopolitical Violence Self-Harm Emotional Dysregulation Trauma Figures Figure 1 Introduction Borderline personality disorder (BPD) is a complex and often debilitating mental health condition marked by fluctuations in mood, self-image, and interpersonal relationships. BPD is characterized by distorted perceptions of self and others, leading to unstable emotional responses, impulsivity, and difficulty maintaining healthy relationships. Fear of abandonment often leads to maladaptive behaviors such as self-harm, suicidal ideation, and intense emotional turmoil [ 1 ][ 2 ]. Although symptoms of BPD may appear in early adolescence, many individuals do not begin treatment until the age of 18. Studies show that over 30% of those diagnosed with BPD engaged in self-harming behaviors before the age of 13, and another 30% between the ages of 13 and 17 [ 2 ]. According to the DSM-5, BPD may be diagnosed as early as age 12 if the symptoms are persistent for at least one year [ 3 ]. However, cross-sectional research indicates that the prevalence of BPD symptoms tends to decline from mid-adulthood to older age, potentially due to changes in symptom expression, mortality rates, and improved coping mechanisms [ 4 ]. Numerous risk factors are associated with the early onset of BPD, including childhood abuse, low socioeconomic status, and family dysfunction [ 5 ]. A strong correlation has been found between lower socioeconomic status and the development of BPD symptoms in adolescence, often linked to limited access to care and chronic stress [ 6 ][ 7 ][ 8 ]. Maladaptive parenting—characterized by emotional rejection, harsh discipline, inconsistency, and lack of warmth—is another established contributor to BPD development [ 9 ][ 10 ][ 11 ]. Gender differences in BPD diagnosis are also evident. Although BPD is more commonly diagnosed in females, this may result from biases in diagnostic tools and differences in symptom presentation [ 12 ][ 13 ]. Females are more likely to exhibit internalizing symptoms such as anxiety and eating disorders, whereas males more often demonstrate externalizing symptoms like aggression, substance use, and impulsivity [ 14 ]. Females also tend to seek treatment more often, which may contribute to higher diagnosis rates [ 15 ]. Geographic and social context play a critical role in diagnosing and treating BPD. Urban areas typically have better access to care, while rural or conflict-affected regions, such as Palestine, often face limitations in mental health infrastructure. Social isolation, displacement, and ongoing conflict contribute to heightened psychological distress in these populations [ 16 ]. Educational attainment can influence both the risk and mitigation of BPD symptoms. While higher education often facilitates earlier recognition and treatment, BPD symptoms, particularly impulsiveness and emotional instability, are associated with poor academic performance and higher dropout rates. Early-onset BPD is also linked to decreased secondary school completion and future unemployment [ 17 ][ 18 ]. Given this context, understanding the prevalence and demographic associations of BPD symptoms among university students in Palestine provides critical insight into a vulnerable and under-researched population. The unique sociopolitical and structural challenges faced by Palestinian youth, including exposure to occupation-related trauma, economic instability, and limited access to care, necessitate localized research to inform prevention and intervention. Therefore, this study aims to examine the prevalence of BPD symptoms among Palestinian people and explore how psychological, demographic, and contextual factors influence symptom presentation and severity. Methodology Study design and settings A cross-sectional questionnaire-based study was conducted from June to September 2024 to examine the prevalence of BPD symptoms among Palestinian university students. The study involved a survey of Palestinians who were aged 18 years and older. The population included university students from all governorates of Palestine's West Bank. We calculated the sample size using the Roasoft formula (www.raosoft.com), which included a reference proportion of 50%, a 95% confidence interval, and a 5% margin of error. We set the sample size at 385 to effectively represent the broader population and account for potential non-response errors. Ultimately, 538 respondents were enrolled in the study. Data collection instrument A web-based questionnaire, administered via Google Forms and consisting of four sections, was used to survey participants. The questionnaire aimed to gather baseline demographic information, assess BPD symptoms. Section One collected demographic information, including gender, age, academic level, employment status, and marital status. Section two has an instrument to test the symptoms of BPD [19] and MSI-BPD [20]. The survey consisted of 10 Yes or No questions on the MSI-BPD scale as qualifying criteria and then 70-80 questions to assess the symptoms based on the BPQ measure on a 5-point Likert Scale. The Arabic version of the validated questionnaire was distributed to participants via electronic means, including university students’ email, Facebook groups, public websites, WhatsApp groups, university students’ forums, and researchers' social media accounts. The questionnaire's introduction included the study's objectives and assured participants that the data would be evaluated and analyzed anonymously, with no personal identifiers collected. Participants were informed of the voluntary nature of their participation. Submission of responses via Google Forms indicated their voluntary agreement to participate. The data were automatically organized in Google Spreadsheets and subsequently imported into SPSS for statistical analysis. Statistical analysis The data were entered into and analyzed using the Social Sciences Statistical Package (SPSS) version 21, by IBM Corp., Armonk, N.Y., USA. The sample is tested for normality, and it is found to be normally distributed. A pilot study was carried out to evaluate the reliability of the questionnaire and it was found to be reliable (Cronbach alpha: 0.73). Descriptive statistics were used to report sample characteristics (frequencies and percentages). Pearson correlation coefficient and Chi-square were used to assess the relationship between the demographic variables and the attitudinal statements regarding genetic testing, genetic counseling, performing genetic tests, and the probability of conducting genetic testing for cancer. The Pearson correlation coefficient was also utilized to assess the relationship between consent and the level of satisfaction with the service received. A p-value of less than 0.05 is considered significant. Ethical approval All aspects of the study protocol were authorized by the An-Najah National University Institutional Review Board (IRB), Nablus, Palestine (Ref: Med. June 2024/16). The participants provided informed consent prior to their participation. The informed consent form explained the premise of the study and assured the anonymity of the participants. Results Table 1: Sociodemographic and Health-Related Characteristics (Categorical Variables). N = 538 Variable Category Frequency (n) Percentage (%) Gender Female 391 72.7% Male 147 27.3% Marital Status Single 481 89.4% Married 55 10.2% Widow/Divorced 2 0.4% Governorate North WB 300 55.8% Middle WB 42 7.8% South WB 163 30.3% Israel 33 6.1% Address Village 253 47.0% Camp 11 2.0% City 274 50.9% Are you currently a college student? Yes 413 76.8% No 125 23.2% College Medical 323 60.0% Non-Medical 211 39.2% Are you a worker or an employee? Yes 57 10.6% No 481 89.4% Transportation method Public Transport 360 66.9% Private Car 165 30.7% Attacked by the occupation at a checkpoint Yes 91 16.9% No 447 83.1% Arrested by the occupation Yes 10 1.9% No 528 98.1% Live near settlement, bypass road, or border Yes 208 38.7% No 330 61.3% Medication for mood, stress, etc. Yes 124 23.0% No 414 77.0% Age 20 hours 50 9.3% Table 1 presents the descriptive statistics, revealing the study participants' detailed demographic and health-related profiles. The sample comprised 538 individuals, with a higher proportion of females (72.7%) compared to males (27.3%). The majority of participants were single (89.4%), while 10.2% were married, and a small percentage (0.4%) were either widowed or divorced. Geographically, most participants resided in the northern West Bank (55.8%), followed by 30.3% in the southern West Bank, and smaller proportions from the middle West Bank (7.8%) and Israel (6.1%). The residential distribution indicated that 50.9% lived in cities, 47.0% in villages, and only 2.0% in refugee camps. In terms of academic status, a significant majority (76.8%) were current college students, with most pursuing medical studies (60.0%). Only 10.6% of the participants reported being employed or working, while 89.4% were not engaged in employment. Regarding transportation, 66.9% relied on public transport, and 30.7% used private cars, reflecting varying levels of mobility and access. Health-related experiences included 16.9% of the participants being attacked at checkpoints, and 1.9% had been arrested by the occupation forces. Additionally, 38.7% reported living near settlements or bypassing roads, indicating potential exposure to security threats. From a health perspective, 23.0% of participants were taking medication for mood, stress, or related conditions. . These descriptive statistics highlight the diverse demographic and health-related characteristics within the sample, providing a strong foundation for understanding how these factors may influence psychological outcomes. Table 2. Correlation Matrix (Spearman's rho). N = 538 Variable 1 2 3 4 5 6 7 8 9 1. Affective Instability 1.000 2. Abandonment .375** 1.000 3. Relationship .297** .563** 1.000 4. Self Image .284** .438** .330** 1.000 5. Suicide/Self-Mutilation .277** .386** .311** .356** 1.000 6. Emptiness .432** .496** .384** .662** .329** 1.000 7. Intense Anger .526** .327** .325** .288** .251** .323** 1.000 8. Impulsivity .272** .299** .346** .253** .312** .272** .310** 1.000 9. Quasi Psychotic Status .373** .359** .277** .275** .338** .353** .225** .324** 1.000 **, p value (2-tailed) is significant at the 0.01 level (p < .01). Table 2 presents the Spearman's rank correlation analysis, which was conducted to examine the relationships among the psychological variables. Significant positive correlations were found between affective instability and various traits, including intense anger (r = .526, p < 0.01) and emptiness (r = .432, p < 0.01). Similarly, abandonment was moderately correlated with relationship issues (r = .563, p < 0.01) and emptiness (r = .496, p < 0.01). These findings indicate that individuals with higher affective instability or abandonment concerns are more likely to experience challenges in relationships, impulsiveness, and self-image, as well as report intense anger and feelings of emptiness. Each variable demonstrated consistent interrelationships, reinforcing the complexity of emotional instability and its associated behavioral manifestations. Table 3. Comparative Analysis of Psychological Factors Across Demographic Groups Factor Affective Instability Abandonment Relationship Self-Image Suicide/Self-Mutilation Emptiness Intense Anger Impulsivity Psychotic Status Gender Female (Mean Rank) 297.23 282.86 274.16 274.36 274.60 283.60 284.80 266.96 268.79 Male (Mean Rank) 195.74 233.97 257.10 256.56 255.92 231.99 228.81 276.26 271.40 p-Value <0.001 0.001 0.255 0.233 0.154 0.001 <0.001 0.529 0.861 College Student Status Student (Mean Rank) 273.72 271.56 273.65 271.00 267.31 269.92 270.25 276.06 269.86 Non-Student (Mean Rank) 255.56 262.69 255.77 264.53 276.72 268.12 267.04 247.82 268.30 p-Value 0.252 0.574 0.258 0.681 0.496 0.909 0.839 0.070 0.921 Occupation Employed (Mean Rank) 239.74 262.53 275.96 265.50 271.36 257.33 282.90 286.06 270.74 Unemployed (Mean Rank) 273.03 270.33 268.73 269.97 269.28 270.94 267.91 267.54 269.35 p-Value 0.126 0.719 0.739 0.836 0.913 0.531 0.490 0.386 0.949 Arrest History Yes (Mean Rank) 302.50 341.30 354.35 314.45 242.90 279.90 281.60 291.00 355.60 No (Mean Rank) 268.88 268.14 267.89 268.65 270.00 269.30 269.27 269.09 267.87 p-Value 0.497 0.138 0.081 0.353 0.530 0.830 0.803 0.653 0.075 Settlement Status Urban (Mean Rank) 260.17 268.51 269.76 272.63 263.43 269.56 265.24 264.39 259.89 Rural (Mean Rank) 284.30 271.06 269.09 277.35 299.31 269.23 290.40 294.58 316.68 p-Value 0.129 0.001 0.133 0.595 0.021 0.985 0.159 0.085 <0.001 Medication No (Mean Rank) 251.41 256.22 259.43 260.26 260.05 259.63 256.97 256.34 257.21 Yes (Mean Rank) 329.90 313.84 303.11 300.33 301.05 302.46 311.35 313.45 310.53 p-Value <0.001 <0.001 0.006 0.011 0.003 0.007 0.001 <0.001 0.001 Mann-Whitney was used The results in Table 3 highlight several significant relationships between affective and psychological factors and demographic variables such as gender, settlement status, and history of medication use for mood and stress-related issues. For example, significant differences in mean ranks were found for gender across multiple factors, such as affective instability (p < 0.001), abandonment (p = 0.001), and intense anger (p < 0.001). Females consistently had higher mean ranks than males in these areas, suggesting they may experience more intense emotional or psychological symptoms. This gender difference may reflect underlying biological or social factors contributing to the heightened emotional instability seen in women compared to men. In terms of settlement status, significant differences were found in abandonment (p = 0.001) and suicide/self-mutilation (p = 0.021), as well as quasi-psychotic status (p < 0.001). Rural residents generally had higher mean ranks than urban residents in these areas, indicating that those living in rural areas might experience higher levels of emotional distress, potentially due to limited access to mental health services or support systems. This finding underscores the importance of addressing mental health disparities in rural versus urban populations. Most notably, the use of medications for mood, stress, and related issues showed significant associations with all factors examined. Those who had taken medications had consistently higher mean ranks than those who had not, particularly for affective instability (p < 0.001), impulsivity (p < 0.001), and intense anger (p = 0.001). These results suggest that individuals who resort to medications for mental health reasons may experience higher levels of emotional instability and distress. This could point to either a higher baseline severity of symptoms or the potential for residual symptoms despite medication use, emphasizing the complexity of treating psychological issues with medication alone. Table 4. Cont. Mean Ranks and Test Statistics for Various Factors by Demographic Variables Factor Affective Instability Abandonment Relationship Self-Image Suicide/Self-Mutilation Emptiness Intense Anger Impulsivity Quasi-Psychotic Status Marital Status Widow (Mean Rank) 225.00 453.50 366.00 465.00 472.50 409.50 248.00 272.50 151.00 Single (Mean Rank) 270.81 274.57 274.06 275.55 272.38 274.12 271.02 274.04 274.75 Married (Mean Rank) 254.83 218.88 227.60 214.17 236.95 225.37 253.45 225.23 225.58 Divorced (Mean Rank) 489.50 429.00 284.00 206.50 472.50 332.00 444.00 519.00 280.00 p-Value 0.455 0.031 0.184 0.023 0.048 0.119 0.590 0.052 0.134 Governorate North WB (Mean Rank) 272.46 275.91 279.17 274.12 274.88 278.53 275.49 275.35 267.74 Middle WB (Mean Rank) 246.40 261.11 260.00 227.20 244.90 219.98 243.52 255.46 246.56 South WB (Mean Rank) 275.43 258.89 257.33 278.46 262.59 272.01 262.14 254.77 272.38 others (Mean Rank) 242.76 274.30 253.77 237.08 286.00 238.08 284.44 306.92 300.48 p-Value 0.521 0.696 0.445 0.143 0.427 0.082 0.513 0.230 0.504 Address Village (Mean Rank) 273.89 271.34 275.78 272.23 271.48 268.13 272.57 278.76 276.35 Camp (Mean Rank) 305.55 268.23 283.18 226.09 329.36 271.41 196.09 237.09 256.73 City (Mean Rank) 264.00 267.85 263.15 268.72 265.27 270.68 269.61 262.25 263.69 p-Value 0.566 0.967 0.618 0.620 0.290 0.982 0.278 0.359 0.619 Academic Year School (Mean Rank) 224.08 288.04 278.04 288.04 224.08 278.04 270.30 278.76 278.04 1st Year University (Mean Rank) 262.90 261.63 261.63 262.90 236.83 261.63 278.04 261.63 261.63 2nd Year University (Mean Rank) 254.55 240.37 240.37 254.55 254.55 240.37 270.30 240.37 240.37 p-Value 0.455 0.011 0.065 0.005 0.048 0.427 0.590 0.052 0.134 Factor Affective Instability Abandonment Relationship Self-Image Suicide/Self-Mutilation Emptiness Intense Anger Impulsivity Quasi-Psychotic Status Age Less than 18 (Mean Rank) 252.85 284.90 279.21 288.76 326.61 280.51 267.01 302.84 310.41 18-25 (Mean Rank) 274.17 273.67 272.74 272.13 269.06 271.22 270.39 270.38 271.66 25-35 (Mean Rank) 232.02 200.55 233.02 248.73 227.82 276.75 248.02 227.13 206.25 35-50 (Mean Rank) 243.94 233.41 210.53 180.16 242.53 180.22 265.41 227.75 234.28 More than 50 (Mean Rank) 296.88 309.63 319.13 295.88 168.00 269.25 371.88 321.88 234.00 Transportation Type PUBLIC TRANSPORTATION 275.28 274.89 275.53 276.73 281.27 282.56 267.56 272.39 273.52 WALKING 253.38 348.63 337.38 256.50 300.19 309.19 271.69 271.88 267.31 PRIVATE CAR 249.60 245.82 244.98 246.27 234.27 231.00 265.54 255.01 252.75 p-Value 0.200 0.041 0.045 0.104 0.001 0.001 0.987 0.471 0.353 Table 4 shows that the results from the Kruskal-Walli’s test indicate significant differences across various demographic factors, particularly in the domains of abandonment, self-image, and suicide/self-mutilation. For marital status, widowed and divorced participants showed higher mean ranks for several factors, with significant differences found in abandonment (p = 0.031), self-image (p = 0.023), and suicide/self-mutilation (p = 0.048). These findings suggest that marital status plays an important role in how individuals experience abandonment and self-image issues, as well as self-destructive tendencies, with widowed and divorced individuals being particularly affected. Age also showed significant associations with suicide/self-mutilation (p = 0.012), indicating that younger participants, especially those under 18, had higher mean ranks compared to older age groups, while older participants (over 50) had the lowest mean ranks in this domain. This suggests a possible age-related variation in self-harm tendencies, where younger individuals may be more prone to suicidal behaviors. Other factors, such as quasi-psychotic status (p = 0.062) and impulsivity (p = 0.206), approached significance but did not reach conventional levels of statistical significance. These patterns highlight the influence of both marital status and age on various psychological factors. The analysis of transportation types revealed significant differences across several psychological factors. For instance, abandonment, relationship issues, suicide/self-mutilation, and emptiness exhibited notable variations based on transportation methods, with p-values indicating statistical significance (p < 0.05) in these areas. Specifically, individuals using public transportation and walking showed higher mean ranks in abandonment, relationship issues, and emptiness, while those using private cars generally had lower mean ranks. Conversely, affective instability, self-image, intense anger, impulsivity, and quasi-psychotic status did not show significant differences across transportation methods (p > 0.05). These results suggest that transportation mode may influence certain psychological factors, highlighting potential areas for further investigation into how daily experiences impact mental health. Table 5. Linear Regression Analysis of Factors Predicting Suicide/Self-Mutilation Predictor B Std. Error Beta t P value Tolerance VIF Impulsivity 0.236 0.093 0.115 2.533 0.012 0.752 1.330 Intense Anger 0.030 0.041 0.036 0.736 0.462 0.655 1.527 Emptiness -0.002 0.056 -0.002 -0.040 0.968 0.402 2.489 Self-Image 0.213 0.057 0.211 3.719 <0.001 0.481 2.079 Relationship 0.002 0.048 0.002 0.034 0.973 0.582 1.719 Abandonment 0.247 0.066 0.220 3.757 <0.001 0.450 2.220 Affective instability 0.011 0.043 0.014 0.264 0.792 0.561 1.782 Quasi Psychotic Status 0.145 0.051 0.135 2.845 0.005 0.683 1.465 Age -0.168 0.064 -0.132 -2.640 0.009 0.620 1.614 Marital Status 0.173 0.095 0.085 1.818 0.070 0.702 1.424 Gender -0.033 0.048 -0.031 -0.686 0.493 0.778 1.285 Have you been attacked by the occupation? 0.048 0.055 0.038 0.886 0.376 0.841 1.189 Have you been arrested before by the occupation? -0.228 0.141 -0.069 -1.615 0.107 0.839 1.192 Do you live near a settlement? -0.011 0.041 -0.011 -0.261 0.794 0.881 1.135 Have you ever taken mood-improving medications? 0.041 0.047 0.037 0.885 0.376 0.909 1.100 Blood group 0.000 0.006 -0.001 -0.017 0.986 0.963 1.038 The governorate -0.019 0.019 -0.043 -1.010 0.313 0.852 1.173 Address 0.022 0.021 0.045 1.038 0.300 0.826 1.211 College 0.029 0.042 0.029 0.697 0.486 0.878 1.139 Academic year 0.011 0.016 0.032 0.677 0.499 0.711 1.406 If you work, how many hours do you work? 0.027 0.037 0.032 0.729 0.466 0.788 1.269 Usual transportation method -0.043 0.022 -0.082 -1.967 0.050 0.900 1.111 Overall Model R R Square Adjusted R Square Std. Error of the Estimate F P value 0.557 0.310 0.276 0.40457 9.104 0.000 The regression analysis in Table 5 shows that the model predicts suicide/self-mutilation with a moderate level of accuracy, as indicated by an R² value of 0.310. This means that approximately 31% of the variance in suicide/self-mutilation can be explained by the predictors included in the model. The predictors encompass a wide range of variables such as emotional states (e.g., impulsivity, intense anger, emptiness), personal demographics (e.g., age, gender, marital status), and experiences with occupation-related stressors (e.g., being attacked or arrested by the occupation). The ANOVA results confirm that the regression model is statistically significant (F = 9.104, p < 0.001), indicating that the predictors collectively have a meaningful impact on the dependent variable. Among the predictors, self-image, abandonment, and quasi-psychotic status are particularly significant, with coefficients showing strong relationships with the outcome variable. Notably, the coefficient for self-image is highly significant (p < 0.001), suggesting that individuals with negative self-image are more likely to report higher levels of suicide/self-mutilation. Similarly, impulsiveness and abandonment also show significant relationships, highlighting the importance of emotional regulation and personal experiences in understanding the risk of self-harm. Collinearity diagnostics indicate that multicollinearity is not a major concern, with all variance inflation factors (VIF) well below the threshold of 10. This suggests that the predictors do not excessively overlap, allowing for reliable interpretation of their individual effects on suicide/self-mutilation. Overall, the model provides valuable insights into the factors influencing self-harm behaviors, underscoring the complex interplay of emotional, demographic, and situational variables. Further research could refine these predictors and explore additional factors to enhance the predictive power and understanding of suicide/self-mutilation. Optimal Model Fit for Predicting Suicide and Self-Mutilation: A Path Analysis Using IBM AMOS The strong fit of the model in Table 6 indicates a high degree of accuracy in representing the complex relationships among psychological variables, such as impulsivity, quasi-psychotic status, and suicide risk. This exceptional fit is reflected in the model’s fit indices, including a CMIN/DF ratio of .804, which is well within the acceptable range, suggesting that the model provides an excellent approximation of the observed data. The low RMR (.005) and high GFI (.999) further confirm that the residuals are minimal, and the model captures the data's underlying structure effectively. The CFI and IFI, both being 1,000, indicate that the model explains the observed data as well as possible, without any improvements needed. Additionally, the RMSEA value of .000, with its associated confidence interval, reinforces that the model fits the data perfectly, suggesting no room for improvement in fit. Table 6. Model Fit Summary Table Metric Default Model Interpretation CMIN 2.411 Excellent fit (CMIN/DF = .804, p = .492) RMR .005 Good fit (low residuals) GFI .999 High goodness of fit CFI 1.000 Excellent model fit RMSEA .000 Excellent fit (PCLOSE = .854) AIC 38.411 Model comparison indicator ECVI .072 Effective fit considering sample size HOELTER .05 1741 Adequate sample size for model fit HOELTER .01 2527 Robust sample size for model validity Model Interpretation The path analysis model in Figure 1 reveals intricate relationships among psychological factors and their impact on suicide/self-mutilation tendencies. Feelings of abandonment are significantly associated with self-image, indicating that distress related to abandonment negatively impacts one’s self-image. Additionally, feelings of abandonment significantly influence impulsiveness, suggesting that higher distress related to abandonment is linked to increased impulsive behaviors. A negative self-image also contributes to higher impulsivity. The model further highlights that impulsivity has a strong positive effect on quasi-psychotic symptoms, and abandonment-related distress also affects quasi-psychotic symptoms. Notably, the method of transportation (public transportation, walking, or using a private car) has a negative effect on both feelings of abandonment and suicide/self-mutilation tendencies, suggesting that transportation methods subtly influence these psychological factors. Overall, the model effectively elucidates how abandonment, self-image, impulsivity, and quasi-psychotic symptoms interrelate and contribute to suicide/self-mutilation tendencies. Table 7. Maximum Likelihood Estimates for the Path Analysis Model Path Estimate S.E. C.R. P ABONDT ← Transportation -0.043 0.019 -2.240 0.025 SIT ← ABONDT 0.583 0.040 14.506 p < 0.001 Impulsivity ← ABONDT 0.156 0.026 5.994 p < 0.001 Impulsivity ← SIT 0.056 0.024 2.367 0.018 Quasi-Psychotic StatusT ← Impulsivity T 0.496 0.078 6.342 p < 0.001 Quasi-Psychotic StatusT ← ABONDT 0.234 0.049 4.806 p < 0.001 Quasi-Psychotic StatusT ← SIT 0.110 0.043 2.556 0.011 SuicideSelfT ← ImpulsivityT 0.282 0.082 3.428 p < 0.001 SuicideSelfT ← Transportation -0.042 0.018 -2.307 0.021 SuicideSelfT ← QuasiPsychoticStatusT 0.147 0.044 3.364 p < 0.001 SuicideSelfT ← SIT 0.207 0.044 4.703 p < 0.001 SuicideSelfT ← ABONDT 0.251 0.051 4.952 p < 0.001 Table 7 presents key mediators in the model, including impulsivity, quasi-psychotic symptoms, and self-image. Impulsivity mediates the relationship between feelings of abandonment and quasi-psychotic symptoms, with heightened impulsivity resulting from increased feelings of abandonment, which subsequently intensifies quasi-psychotic symptoms. Quasi-psychotic symptoms also mediate the relationship between impulsivity and suicide/self-mutilation tendencies. Additionally, self-image impacts both impulsive and quasi-psychotic symptoms, thereby influencing suicide/self-mutilation tendencies. These mediators elucidate the complex psychological mechanisms at play, highlighting how emotional and cognitive factors interact to affect suicide/self-mutilation outcomes. Discussion This study contributes to a growing body of evidence on the prevalence and psychological correlations of borderline personality disorder (BPD) symptoms among populations exposed to chronic sociopolitical adversity. The findings reveal that emotional dysregulation, abandonment fears, and self-harming behaviors are significantly associated with demographic factors, medication use, and exposure to sociopolitical violence. These results are broadly consistent with previous clinical and empirical studies in conflict-affected contexts. Gender and Emotional Dysregulation The data confirmed that female participants reported significantly higher levels of affective instability, abandonment concerns, and intense anger. These findings align with previous research that suggests females are more likely to exhibit internalizing symptoms of BPD and report greater emotional vulnerability compared to males (Sansone & Sansone, 2011; Bozzatello et al., 2024). This gender disparity may stem from both biological sensitivity and greater exposure to interpersonal stressors, as well as diagnostic biases that underreport BPD in men (Jane et al., 2007). These findings underscore the need for gender-sensitive screening tools and interventions. Sociopolitical Violence and BPD Symptoms Participants exposed to occupation-related trauma (e.g., attacks, arrests, or proximity to settlements) demonstrated significantly higher scores in abandonment, suicidal ideation, and quasi-psychotic symptoms. These results reinforce the role of external stressors in the onset and exacerbation of BPD symptoms, consistent with literature linking trauma exposure and emotional instability in high-conflict regions (Sousa, 2013; Dubow et al., 2009). The unique Palestinian sociopolitical context—characterized by displacement, restricted mobility, and chronic insecurity—amplifies emotional vulnerability and justifies the integration of trauma-informed approaches in treatment programs. Psychological Predictors of Self-Harm Multivariate regression and path analysis identified poor self-image, feelings of abandonment, quasi-psychotic symptoms, and impulsivity as significant predictors of suicide/self-mutilation behaviors. These results are congruent with prior studies demonstrating that disturbances in identity and cognition are central to self-harming tendencies in BPD patients (Suarez & Feixas, 2020). Although impulsiveness had a weaker predictive value than anticipated, it served as a key mediator in the pathways from abandonment and self-image to quasi-psychotic symptoms and suicidality. This complexity supports the view that interventions targeting impulsive alone may not suffice; instead, programs should also address cognitive conflict and self-schema disturbances that fuel emotional instability. Medication Use and Unresolved Distress A noteworthy and somewhat paradoxical finding was that participants who used medication for mood or stress reported higher levels of psychological distress across all domains. This may reflect a treatment bias, where individuals with more severe symptoms are more likely to be prescribed medications. Alternatively, it may indicate limited effectiveness of pharmacotherapy when not integrated with psychosocial interventions echoed by Paton et al. (2015). These results highlight the necessity of combining medication with evidence-based psychotherapies such as DBT or CBT. Rural Residency and Limited Access to Care Participants residing in rural areas showed higher levels of abandonment fears, suicidal ideation, and quasi-psychotic symptoms compared to urban residents. This supports previous research indicating that structural limitations in rural mental health services exacerbate emotional distress (Lötzer, 2023). Culturally sensitive, community-based mental health interventions are urgently needed in these regions. Transportation and Socioeconomic Disadvantage An unexpected yet important finding was the association between transportation methods and psychological symptoms. Individuals relying on public transportation or walking reported greater emotional distress than those using private cars. This suggests that transportation may be a proxy for broader socioeconomic hardship, with longer commutes and lower mobility reflecting chronic daily stress. This novel result invites further research into how seemingly peripheral socioeconomic factors influence mental health in conflict zones. Social Networks and Therapeutic Potential Although not a primary focus, the results indirectly support Beeney et al.’s (2018) findings on the role of social disadvantage and interpersonal isolation in BPD symptomatology. In the Palestinian context, where sociopolitical trauma restricts social cohesion, strengthening interpersonal support systems may mitigate symptoms and improve therapy outcomes. Implications for Clinical Practice and Policy The study’s results highlight the importance of addressing the psychological consequences of sociopolitical trauma through contextually adapted mental health policies. Training healthcare professionals to recognize and respond empathetically to BPD symptoms—especially in marginalized populations—is vital, as stigma among providers can undermine treatment effectiveness (Aljohani et al., 2022). Limitations and Future Directions Several limitations should be noted, including the use of non-parametric tests due to non-normal data may limit generalizability. The cross-sectional design prevents causal inference. The sample skew toward single, medical students may limit population-wide applicability. Future studies should adopt longitudinal designs, expand demographic representation, and explore interventions that integrate trauma-informed, gender-sensitive, and culturally grounded approaches. Conclusion This study provides robust evidence on the psychological, demographic, and contextual determinants of borderline personality disorder (BPD) symptoms among Palestinian university students. It demonstrates that females, individuals exposed to sociopolitical violence, and those residing in rural areas or using psychiatric medication reported higher levels of affective instability, abandonment, and self-harming tendencies. Key psychological predictors of self-harm included disturbed self-image, impulsivity, and quasi-psychotic symptoms, emphasizing the urgent need for integrated interventions that target both emotional regulation and cognitive conflicts. The findings also uncovered the significant yet underexplored role of socioeconomic indicators, such as transportation methods, as stressors linked to mental health. Given the sociopolitical complexities and limited mental health infrastructure in Palestine, there is a critical need for culturally adapted, trauma-informed, and community-based mental health services. Future research should prioritize longitudinal designs to better understand symptom trajectories and the long-term effectiveness of tailored interventions in conflict-affected populations. Abbreviations BPD: borderline personality disorder BPQ: borderline personality questionnaire MSI-BPD: McLean Screening Instrument for borderline personality disorder Declarations Clinical trial number: Not applicable Ethics approval and consent to participate This study was approved by the Institutional Review Board (IRB) of An-Najah National University (approval number: Med. June 2023/16). Informed consent was obtained from all participants prior to their involvement in the study. All participants consented to the anonymous use of their data for research purposes, with the assurance that the data would only be used for clinical research and publication following the Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials All data from the current work is obtainable from the corresponding author upon request. [email protected] Competing interests The authors declare that they have no competing interests. Funding None Authors’ contributions AAH, MA, AS, TK, NJ, and FA contributed to the literature review, data collection, data analysis, and initial drafting of the manuscript. AAH, MA, AS, TK, and NJ were involved in study design. They also drafted the manuscript, ensured data integrity, and critically reviewed the research to enhance its intellectual content. AAH conceptualized the study, ensured data integrity, and provided critical intellectual input. He also conceived and designed the research, supervised and coordinated data analysis, AAH, and FA critically reviewed the interpretation of results, and assisted with the final manuscript preparation. All authors reviewed and approved the final manuscript. Acknowledgements We would like to express our sincere gratitude to An-Najah National University (www.najah.edu) and all participating hospitals for their invaluable support in providing resources and facilitating the research process. References Chapman J, Jamil RT, Fleisher C. Borderline Personality Disorder. 2023 Jun 2. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan. Biskin, RS. The Lifetime Course of Borderline Personality Disorder. Can J Psychiatry. 2015 Jul;60(7):303-8. doi: 10.1177/070674371506000702. PMID: 26175388; PMCID: PMC4500179. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596 Videler AC, Hutsebaut J, Schulkens JEM, Sobczak S, van Alphen SPJ. A Life Span Perspective on Borderline Personality Disorder. Curr Psychiatry Rep. 2019 Jun 4;21(7):51. doi: 10.1007/s11920-019-1040-1. PMID: 31161404; PMCID: PMC6546651. Bozzatello P, Garbarini C, Rocca P, Bellino S. Borderline Personality Disorder: Risk Factors and Early Detection. Diagnostics (Basel). 2021 Nov 18;11(11):2142. doi: 10.3390/diagnostics11112142. PMID: 34829488; PMCID: PMC8620075. Cohen P., Chen H., Gordon K., Johnson J., Brook J., Kasen S. Socioeconomic background and the developmental course of schizotypal and borderline personality disorder symptoms. Dev. Psychopathol. 2008;20:633–650. doi: 10.1017/S095457940800031X Crawford T.N., Cohen P.R., Chen H., Anglin D.M., Ehrensaft M. Early maternal separation and the trajectory of borderline personality disorder symptoms. Dev. Psychopathol. 2009;21:1013–1030. doi: 10.1017/S0954579409000546 Paris J. Personality disorders over time: precursors, course and outcome. J Pers Disord. 2003 Dec;17(6):479-88. doi: 10.1521/pedi.17.6.479.25360. PMID: 14744074. Steele K.R., Townsend M.L., Grenyer B.F.S. Parenting and personality disorder: An overview and meta-synthesis of systematic reviews. PLoS ONE. 2019;14:e0223038. doi: 10.1371/journal.pone.0223038. Vanwoerden S., Kalpakci A., Sharp C. The relations between inadequate parent-child boundaries and borderline personality disorder inadolescence. Psychiatry Res. 2017;257:462–471. doi: 10.1016/j.psychres.2017.08.015 Lyons-Ruth K., Bureau J.-F., Holmes B., Easterbrooks A., Brooks N.H. Borderline symptoms and suicidality/self-injury in late adolescence: Prospectively observed relationship correlates in infancy and childhood. Psychiatry Res. 2013;206:273–281. doi: 10.1016/j.psychres.2012.09.030. Bozzatello P, Blua C, Brandellero D, Baldassarri L, Brasso C, Rocca P, Bellino S. Gender differences in borderline personality disorder: a narrative review. Front Psychiatry. 2024 Jan 12;15:1320546. doi: 10.3389/fpsyt.2024.1320546. PMID: 38283847; PMCID: PMC10811047. Jane JS, Oltmanns TF, South SC, Turkheimer E. Gender bias in diagnostic criteria for personality disorders: an item response theory analysis. J Abnorm Psychol. (2007) 116:166–75. doi: 10.1037/0021-843X.116.1.166 Sansone RA, Sansone LA. Gender patterns in borderline personality disorder. Innov Clin Neurosci. 2011 May;8(5):16-20. PMID: 21686143; PMCID: PMC3115767. Tedstone Doherty D, Kartalova-O'Doherty Y. Gender and self-reported mental health problems: predictors of help seeking from a general practitioner. Br J Health Psychol. 2010 Feb;15(Pt 1):213-28. doi: 10.1348/135910709X457423. Epub 2009 Jun 12. PMID: 19527564; PMCID: PMC2845878. Lenzenweger MF, Lane MC, Loranger AW, Kessler RC. DSM-IV personality disorders in the National Comorbidity Survey Replication. Biol Psychiatry. 2007 Sep 15;62(6):553-64. doi: 10.1016/j.biopsych.2006.09.019. Epub 2007 Jan 9. PMID: 17217923; PMCID: PMC2044500. Chanen, A.M., Nicol, K., Betts, J.K. et al. INdividual Vocational and Educational Support Trial (INVEST) for young people with borderline personality disorder: study protocol for a randomised controlled trial. Trials 21, 583 (2020). https://doi.org/10.1186/s13063-020-04471-3 Hastrup, L.H., Jennum, P., Ibsen, R. et al. Welfare consequences of early-onset Borderline Personality Disorder: a nationwide register-based case-control study. Eur Child Adolesc Psychiatry 31, 253–260 (2022). https://doi.org/10.1007/s00787-020-01683-5 Poreh AM, R. D. (2006). The BPQ: A scale for the assessment of borderline personality based on DSM-IV. guilford journals, Volume 20. Zanarini, M. C. (2003). A Screening Measure for BPD: The McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD). . guilford journals, Volume 17. Sansone, Randy A, and Lori A Sansone. “Gender Patterns in Borderline Personality Disorder.” Innovations in Clinical Neuroscience, vol. 8, no. 5, May 2011, p. 16, pmc.ncbi.nlm.nih.gov/articles/PMC3115767/. “PSV va News - Summer 2018 - Are There Gender Differences in Borderline Personality Disorder?” Psva.org, psva.org/wp-content/uploads/2018/summer/news2.html. Sousa, Cindy A. “Political Violence, Collective Functioning and Health: A Review of the Literature.” Medicine, Conflict and Survival, vol. 29, no. 3, Sept. 2013, pp. 169–197, https://doi.org/10.1080/13623699.2013.813109. Dubow, Eric F., et al. “Exposure to Conflict and Violence across Contexts: Relations to Adjustment among Palestinian Children.” Journal of Clinical Child & Adolescent Psychology, vol. 39, no. 1, 31 Dec. 2009, pp. 103–116, https://doi.org/10.1080/15374410903401153 . Accessed 22 Mar. 2019. Suarez, Victor, and Guillem Feixas. “Cognitive Conflict in Borderline Personality Disorder: A Study Protocol.” Behavioral Sciences, vol. 10, no. 12, 26 Nov. 2020, p. 180, https://doi.org/10.3390/bs10120180. Paton, Carol, et al. “The Use of Psychotropic Medication in Patients with Emotionally Unstable Personality Disorder under the Care of UK Mental Health Services.” The Journal of Clinical Psychiatry, vol. 76, no. 04, 22 Apr. 2015, pp. e512–e518, https://doi.org/10.4088/jcp.14m09228. Lötzer, Dorian. “Mental Health in the West Bank and Gaza – ISDC.” ISDC, 10 Feb. 2023, isdc.org/publications/mental-health-in-the-west-bank-and-gaza/. Accessed 27 Feb. 2025. “Findings by Dr. Joseph E. Beeney Show That Individuals with Borderline Personality Disorder Are at a Social Disadvantage.” University of Pittsburgh Department of Psychiatry, 2018, www.psychiatry.pitt.edu/findings-dr-joseph-e-beeney-show-individuals-borderline-personality-disorder-are-social. Accessed 27 Feb. 2025. Beeney, Joseph E., et al. “Social Disadvantage and Borderline Personality Disorder: A Study of Social Networks.” Personality Disorders: Theory, Research, and Treatment, vol. 9, no. 1, Jan. 2018, pp. 62–72, www.ncbi.nlm.nih.gov/pmc/articles/PMC5468502/, https://doi.org/10.1037/per0000234. Aljohani, Enas M, et al. “Mental Health Workers’ Knowledge and Attitude towards Borderline Personality Disorder: A Saudi Multicenter Study.” Cureus, vol. 14, no. 11, 27 Nov. 2022, https://doi.org/10.7759/cureus.31938. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Jul, 2025 Reviewers invited by journal 04 Jul, 2025 Editor invited by journal 09 Jun, 2025 Editor assigned by journal 06 Jun, 2025 Submission checks completed at journal 06 Jun, 2025 First submitted to journal 25 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6746037","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482578319,"identity":"8317aa94-9d8a-4ab7-a280-327ea636140a","order_by":0,"name":"Ahmad A. Hanani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFACHgjJDyQMGBuA5AFitUg2kKqFwQCkkigt/A28Bz8X1NyRMb7d/KDg5w4GOb4bCfi1SBzgS5aecewZj9mdYwaGvWcYjCUJaWE4wGMgzcN2mMfsRoKBAW8bQ+IGQlrkD/AY/+b5d5jHeEb6B8O/bQz1BLUYHOAxk+ZtO8xjIJFjYAy0JcGAkBZDoJOsefsO80jcyCkwlm2TMJx55gF+LXLHe4xv83w7bM8/I32b4ds2G3m+4wRsYWBGMNkMgCFIQDm6bgIuGgWjYBSMgpEKAIAgQpgf3EsGAAAAAElFTkSuQmCC","orcid":"","institution":"An-Najah National University","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"A.","lastName":"Hanani","suffix":""},{"id":482578321,"identity":"c8f612a0-a70a-461c-b798-d6a2723553c6","order_by":1,"name":"Merna Al-Rashayda","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Merna","middleName":"","lastName":"Al-Rashayda","suffix":""},{"id":482578323,"identity":"b5bdaa82-01bb-403a-a08f-70b699a80324","order_by":2,"name":"Asalah Shhadi","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Asalah","middleName":"","lastName":"Shhadi","suffix":""},{"id":482578324,"identity":"01cb5592-7985-4e02-b424-a25f6a33d058","order_by":3,"name":"Tala Karaka","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Tala","middleName":"","lastName":"Karaka","suffix":""},{"id":482578325,"identity":"b5108d97-983d-42f0-acc4-585da2058b03","order_by":4,"name":"Nooredin Jomaa","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Nooredin","middleName":"","lastName":"Jomaa","suffix":""},{"id":482578326,"identity":"2179c1d5-a290-4191-9e8b-4b498fda7daa","order_by":5,"name":"Faten Amer","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Faten","middleName":"","lastName":"Amer","suffix":""}],"badges":[],"createdAt":"2025-05-26 00:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6746037/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6746037/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86328729,"identity":"d01e44b7-c1a9-4b2d-8dc4-08404630e09a","added_by":"auto","created_at":"2025-07-09 11:36:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102996,"visible":true,"origin":"","legend":"\u003cp\u003eOptimal Model Fit for Predicting Suicide and Self-Mutilation\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6746037/v1/36fe42380698025a9d85763e.png"},{"id":86329357,"identity":"bd798b3a-6a16-4055-8ddf-83b66a7845d2","added_by":"auto","created_at":"2025-07-09 11:44:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1554822,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6746037/v1/fdc9c48c-a855-4e8c-a8ae-44c88e1ced0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Borderline Personality Disorder Symptoms in a Conflict-Affected Population: A Study Among University Students in a Low-Middle-Income Country","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBorderline personality disorder (BPD) is a complex and often debilitating mental health condition marked by fluctuations in mood, self-image, and interpersonal relationships. BPD is characterized by distorted perceptions of self and others, leading to unstable emotional responses, impulsivity, and difficulty maintaining healthy relationships. Fear of abandonment often leads to maladaptive behaviors such as self-harm, suicidal ideation, and intense emotional turmoil [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough symptoms of BPD may appear in early adolescence, many individuals do not begin treatment until the age of 18. Studies show that over 30% of those diagnosed with BPD engaged in self-harming behaviors before the age of 13, and another 30% between the ages of 13 and 17 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the DSM-5, BPD may be diagnosed as early as age 12 if the symptoms are persistent for at least one year [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, cross-sectional research indicates that the prevalence of BPD symptoms tends to decline from mid-adulthood to older age, potentially due to changes in symptom expression, mortality rates, and improved coping mechanisms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNumerous risk factors are associated with the early onset of BPD, including childhood abuse, low socioeconomic status, and family dysfunction [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A strong correlation has been found between lower socioeconomic status and the development of BPD symptoms in adolescence, often linked to limited access to care and chronic stress [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Maladaptive parenting\u0026mdash;characterized by emotional rejection, harsh discipline, inconsistency, and lack of warmth\u0026mdash;is another established contributor to BPD development [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGender differences in BPD diagnosis are also evident. Although BPD is more commonly diagnosed in females, this may result from biases in diagnostic tools and differences in symptom presentation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Females are more likely to exhibit internalizing symptoms such as anxiety and eating disorders, whereas males more often demonstrate externalizing symptoms like aggression, substance use, and impulsivity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Females also tend to seek treatment more often, which may contribute to higher diagnosis rates [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGeographic and social context play a critical role in diagnosing and treating BPD. Urban areas typically have better access to care, while rural or conflict-affected regions, such as Palestine, often face limitations in mental health infrastructure. Social isolation, displacement, and ongoing conflict contribute to heightened psychological distress in these populations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEducational attainment can influence both the risk and mitigation of BPD symptoms. While higher education often facilitates earlier recognition and treatment, BPD symptoms, particularly impulsiveness and emotional instability, are associated with poor academic performance and higher dropout rates. Early-onset BPD is also linked to decreased secondary school completion and future unemployment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven this context, understanding the prevalence and demographic associations of BPD symptoms among university students in Palestine provides critical insight into a vulnerable and under-researched population. The unique sociopolitical and structural challenges faced by Palestinian youth, including exposure to occupation-related trauma, economic instability, and limited access to care, necessitate localized research to inform prevention and intervention. Therefore, this study aims to examine the prevalence of BPD symptoms among Palestinian people and explore how psychological, demographic, and contextual factors influence symptom presentation and severity.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eStudy design and settings\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional questionnaire-based study was conducted from June to September 2024 to examine the prevalence of BPD symptoms among Palestinian university students. The study involved a survey of Palestinians who were aged 18 years and older. The population included university students from all governorates of Palestine\u0026apos;s West Bank. We calculated the sample size using the Roasoft formula (www.raosoft.com), which included a reference proportion of 50%, a 95% confidence interval, and a 5% margin of error. We set the sample size at 385 to effectively represent the broader population and account for potential non-response errors. Ultimately, 538 respondents were enrolled in the study.\u003c/p\u003e\n\u003cp id=\"_Toc124005272\"\u003e\u003cstrong\u003eData collection instrument\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA web-based questionnaire, administered via Google Forms and consisting of four sections, was used to survey participants. The questionnaire aimed to gather baseline demographic information, assess BPD symptoms. Section One collected demographic information, including gender, age, academic level, employment status, and marital status. Section two has an instrument to test the symptoms of BPD [19] and MSI-BPD [20]. The survey consisted of 10 Yes or No questions on the MSI-BPD scale as qualifying criteria and then 70-80 questions to assess the symptoms based on the BPQ measure on a 5-point Likert Scale. The Arabic version of the validated questionnaire was distributed to participants via electronic means, including university students\u0026rsquo; email, Facebook groups, public websites, WhatsApp groups, university students\u0026rsquo; forums, and researchers\u0026apos; social media accounts. The questionnaire\u0026apos;s introduction included the study\u0026apos;s objectives and assured participants that the data would be evaluated and analyzed anonymously, with no personal identifiers collected. Participants were informed of the voluntary nature of their participation. Submission of responses via Google Forms indicated their voluntary agreement to participate. The data were automatically organized in Google Spreadsheets and subsequently imported into SPSS for statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc124005273\"\u003eThe data were entered into and analyzed using the Social Sciences Statistical Package (SPSS) version 21, by IBM Corp., Armonk, N.Y., USA. The sample is tested for normality, and it is found to be normally distributed. A pilot study was carried out to evaluate the reliability of the questionnaire and it was found to be reliable (Cronbach alpha: 0.73). Descriptive statistics were used to report sample characteristics (frequencies and percentages). Pearson correlation coefficient and Chi-square were used to assess the relationship between the demographic variables and the attitudinal statements regarding genetic testing, genetic counseling, performing genetic tests, and the probability of conducting genetic testing for cancer. The Pearson correlation coefficient was also utilized to assess the relationship between consent and the level of satisfaction with the service received. A p-value of less than 0.05 is considered significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll aspects of the study protocol were authorized by the An-Najah National University Institutional Review Board (IRB), Nablus, Palestine (Ref: Med. June 2024/16). The participants provided informed consent prior to their participation. The informed consent form explained the premise of the study and assured the anonymity of the participants.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eTable 1: Sociodemographic and Health-Related Characteristics (Categorical Variables). N = 538\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eFrequency (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e72.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e27.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e89.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eWidow/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGovernorate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNorth WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e55.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMiddle WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSouth WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e30.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAddress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVillage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e47.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCamp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e50.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAre you currently a college student?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e76.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMedical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e60.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNon-Medical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e39.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAre you a worker or an employee?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e89.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eTransportation method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePublic Transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e66.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePrivate Car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e30.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAttacked by the occupation at a checkpoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e83.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eArrested by the occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e98.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eLive near settlement, bypass road, or border\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e38.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e61.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMedication for mood, stress, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e77.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt; 20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e20-29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e65.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e30-39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ge; 40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eHours Worked per Week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0-10 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e81.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e11-20 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt; 20 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1 presents the descriptive statistics, revealing the study participants\u0026apos; detailed demographic and health-related profiles. The sample comprised 538 individuals, with a higher proportion of females (72.7%) compared to males (27.3%). The majority of participants were single (89.4%), while 10.2% were married, and a small percentage (0.4%) were either widowed or divorced. Geographically, most participants resided in the northern West Bank (55.8%), followed by 30.3% in the southern West Bank, and smaller proportions from the middle West Bank (7.8%) and Israel (6.1%). The residential distribution indicated that 50.9% lived in cities, 47.0% in villages, and only 2.0% in refugee camps.\u003c/p\u003e\n\u003cp\u003eIn terms of academic status, a significant majority (76.8%) were current college students, with most pursuing medical studies (60.0%). Only 10.6% of the participants reported being employed or working, while 89.4% were not engaged in employment. Regarding transportation, 66.9% relied on public transport, and 30.7% used private cars, reflecting varying levels of mobility and access. Health-related experiences included 16.9% of the participants being attacked at checkpoints, and 1.9% had been arrested by the occupation forces. Additionally, 38.7% reported living near settlements or bypassing roads, indicating potential exposure to security threats.\u003c/p\u003e\n\u003cp\u003eFrom a health perspective, 23.0% of participants were taking medication for mood, stress, or related conditions. . These descriptive statistics highlight the diverse demographic and health-related characteristics within the sample, providing a strong foundation for understanding how these factors may influence psychological outcomes.\u003c/p\u003e\n\u003ch3\u003eTable 2. Correlation Matrix (Spearman\u0026apos;s rho). N = 538\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1. Affective Instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2. Abandonment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.375**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3. Relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.297**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.563**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e4. Self Image\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.284**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.438**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.330**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e5. Suicide/Self-Mutilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.277**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.386**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.311**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.356**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6. Emptiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.432**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.496**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.384**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.662**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.329**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e7. Intense Anger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.526**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.327**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.325**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.288**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.251**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.323**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e8. Impulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.272**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.299**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.346**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.253**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.312**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.272**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.310**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e9. Quasi Psychotic Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.373**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.359**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.277**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.275**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.338**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.353**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.225**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e.324**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e**, p value (2-tailed) is significant at the 0.01 level (p \u0026lt; .01).\u003c/p\u003e\n\u003cp\u003eTable 2 presents the\u0026nbsp;Spearman\u0026apos;s rank correlation analysis, which was conducted to examine the relationships among the psychological variables. Significant positive correlations were found between affective instability and various traits, including intense anger (r = .526, p \u0026lt; 0.01) and emptiness (r = .432, p \u0026lt; 0.01). Similarly, abandonment was moderately correlated with relationship issues (r = .563, p \u0026lt; 0.01) and emptiness (r = .496, p \u0026lt; 0.01). These findings indicate that individuals with higher affective instability or abandonment concerns are more likely to experience challenges in relationships, impulsiveness, and self-image, as well as report intense anger and feelings of emptiness. Each variable demonstrated consistent interrelationships, reinforcing the complexity of emotional instability and its associated behavioral manifestations.\u003c/p\u003e\n\u003cp\u003eTable 3. Comparative Analysis of Psychological Factors Across Demographic Groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffective Instability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbandonment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationship\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Image\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuicide/Self-Mutilation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmptiness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntense Anger\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImpulsivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychotic Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eFemale (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e297.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e282.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e274.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e274.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e274.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e283.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e284.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e266.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e268.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eMale (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e195.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e233.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e257.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e256.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e255.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e231.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e228.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e276.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e271.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eCollege Student Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eStudent (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e273.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e271.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e273.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e271.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e267.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e269.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e270.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e276.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e269.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eNon-Student (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e255.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e262.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e255.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e264.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e276.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e268.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e267.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e247.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e268.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eEmployed (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e239.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e262.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e275.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e265.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e271.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e257.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e282.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e286.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e270.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eUnemployed (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e273.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e270.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e268.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e269.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e269.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e270.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e267.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e267.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e269.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eArrest History\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eYes (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e302.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e341.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e354.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e314.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e242.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e279.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e281.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e291.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e355.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eNo (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e268.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e268.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e267.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e268.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e270.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e269.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e269.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e269.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e267.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eSettlement Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eUrban (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e260.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e268.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e269.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e272.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e263.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e269.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e265.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e264.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e259.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eRural (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e284.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e271.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e269.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e277.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e299.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e269.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e290.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e294.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e316.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eMedication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eNo (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e251.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e256.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e259.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e260.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e260.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e259.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e256.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e256.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e257.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eYes (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e329.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e313.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e303.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e300.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e301.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e302.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e311.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e313.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e310.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMann-Whitney was used\u003c/p\u003e\n\u003cp\u003eThe results in Table 3 highlight several significant relationships between affective and psychological factors and demographic variables such as gender, settlement status, and history of medication use for mood and stress-related issues. For example, significant differences in mean ranks were found for gender across multiple factors, such as affective instability (p \u0026lt; 0.001), abandonment (p = 0.001), and intense anger (p \u0026lt; 0.001). Females consistently had higher mean ranks than males in these areas, suggesting they may experience more intense emotional or psychological symptoms. This gender difference may reflect underlying biological or social factors contributing to the heightened emotional instability seen in women compared to men.\u003c/p\u003e\n\u003cp\u003eIn terms of settlement status, significant differences were found in abandonment (p = 0.001) and suicide/self-mutilation (p = 0.021), as well as quasi-psychotic status (p \u0026lt; 0.001). Rural residents generally had higher mean ranks than urban residents in these areas, indicating that those living in rural areas might experience higher levels of emotional distress, potentially due to limited access to mental health services or support systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis finding underscores the importance of addressing mental health disparities in rural versus urban populations. Most notably, the use of medications for mood, stress, and related issues showed significant associations with all factors examined. Those who had taken medications had consistently higher mean ranks than those who had not, particularly for affective instability (p \u0026lt; 0.001), impulsivity (p \u0026lt; 0.001), and intense anger (p = 0.001). These results suggest that individuals who resort to medications for mental health reasons may experience higher levels of emotional instability and distress. This could point to either a higher baseline severity of symptoms or the potential for residual symptoms despite medication use, emphasizing the complexity of treating psychological issues with medication alone.\u003c/p\u003e\n\u003ch3\u003eTable 4. Cont. Mean Ranks and Test Statistics for Various Factors by Demographic Variables\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eAffective Instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eAbandonment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eRelationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSelf-Image\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSuicide/Self-Mutilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eEmptiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eIntense Anger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eImpulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eQuasi-Psychotic Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eWidow (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e225.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e453.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e366.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e465.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e472.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e409.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e248.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e272.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e151.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eSingle (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e270.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e274.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e274.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e275.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e272.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e274.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e271.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e274.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e274.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eMarried (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e254.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e218.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e227.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e214.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e236.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e225.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e253.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e225.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e225.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eDivorced (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e489.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e429.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e284.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e206.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e472.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e332.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e444.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e519.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e280.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eGovernorate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eNorth WB (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e272.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e275.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e279.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e274.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e274.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e278.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e275.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e275.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e267.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eMiddle WB (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e246.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e261.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e260.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e227.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e244.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e219.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e243.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e255.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e246.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eSouth WB (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e275.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e258.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e257.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e278.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e262.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e272.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e262.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e254.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e272.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eothers (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e242.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e274.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e253.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e237.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e286.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e238.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e284.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e306.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e300.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eAddress\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eVillage (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e273.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e271.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e275.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e272.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e271.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e268.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e272.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e278.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e276.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eCamp (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e305.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e268.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e283.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e226.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e329.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e271.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e196.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e237.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e256.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eCity (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e264.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e267.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e263.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e268.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e265.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e270.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e269.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e262.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e263.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eAcademic Year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eSchool (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e224.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e288.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e278.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e288.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e224.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e278.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e270.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e278.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e278.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1st Year University (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e262.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e261.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e261.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e262.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e236.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e261.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e278.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e261.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e261.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e2nd Year University (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e254.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e240.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e240.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e254.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e254.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e240.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e270.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e240.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e240.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eAffective Instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eAbandonment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eRelationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSelf-Image\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSuicide/Self-Mutilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eEmptiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eIntense Anger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eImpulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eQuasi-Psychotic Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" valign=\"top\" style=\"width: 720px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLess than 18 (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e252.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e284.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e279.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e288.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e326.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e280.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e267.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e302.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e310.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e18-25 (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e274.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e273.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e272.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e272.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e269.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e271.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e270.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e270.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e271.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e25-35 (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e232.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e200.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e233.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e248.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e227.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e276.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e248.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e227.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e206.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e35-50 (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e243.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e233.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e210.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e180.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e242.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e180.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e265.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e227.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e234.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMore than 50 (Mean Rank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e296.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e309.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e319.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e295.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e168.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e269.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e371.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e321.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e234.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" valign=\"top\" style=\"width: 720px;\"\u003e\n \u003cp\u003eTransportation Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePUBLIC TRANSPORTATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e275.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e274.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e275.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e276.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e281.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e282.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e267.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e272.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e273.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eWALKING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e253.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e348.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e337.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e256.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e300.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e309.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e271.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e271.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e267.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePRIVATE CAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e249.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e245.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e244.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e246.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e234.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e231.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e265.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e255.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e252.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 shows that the results from the Kruskal-Walli\u0026rsquo;s test indicate significant differences across various demographic factors, particularly in the domains of abandonment, self-image, and suicide/self-mutilation. For marital status, widowed and divorced participants showed higher mean ranks for several factors, with significant differences found in abandonment (p = 0.031), self-image (p = 0.023), and suicide/self-mutilation (p = 0.048). These findings suggest that marital status plays an important role in how individuals experience abandonment and self-image issues, as well as self-destructive tendencies, with widowed and divorced individuals being particularly affected.\u003c/p\u003e\n\u003cp\u003eAge also showed significant associations with suicide/self-mutilation (p = 0.012), indicating that younger participants, especially those under 18, had higher mean ranks compared to older age groups, while older participants (over 50) had the lowest mean ranks in this domain. This suggests a possible age-related variation in self-harm tendencies, where younger individuals may be more prone to suicidal behaviors. Other factors, such as quasi-psychotic status (p = 0.062) and impulsivity (p = 0.206), approached significance but did not reach conventional levels of statistical significance. These patterns highlight the influence of both marital status and age on various psychological factors.\u003c/p\u003e\n\u003cp\u003eThe analysis of transportation types revealed significant differences across several psychological factors. For instance, abandonment, relationship issues, suicide/self-mutilation, and emptiness exhibited notable variations based on transportation methods, with p-values indicating statistical significance (p \u0026lt; 0.05) in these areas. Specifically, individuals using public transportation and walking showed higher mean ranks in abandonment, relationship issues, and emptiness, while those using private cars generally had lower mean ranks. Conversely, affective instability, self-image, intense anger, impulsivity, and quasi-psychotic status did not show significant differences across transportation methods (p \u0026gt; 0.05). These results suggest that transportation mode may influence certain psychological factors, highlighting potential areas for further investigation into how daily experiences impact mental health.\u003c/p\u003e\n\u003ch3\u003eTable 5. Linear Regression Analysis of Factors Predicting Suicide/Self-Mutilation\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eImpulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eIntense Anger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eEmptiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSelf-Image\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eRelationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAbandonment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAffective instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eQuasi Psychotic Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-2.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eHave you been attacked by the occupation?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eHave you been arrested before by the occupation?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-1.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eDo you live near a settlement?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eHave you ever taken mood-improving medications?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eBlood group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eThe governorate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAddress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eCollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAcademic year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eIf you work, how many hours do you work?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eUsual transportation method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-1.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 658px;\"\u003e\n \u003ch3\u003eOverall Model\u003c/h3\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAdjusted R Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.40457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe regression analysis in Table 5 shows that the model predicts suicide/self-mutilation with a moderate level of accuracy, as indicated by an R\u0026sup2; value of 0.310. This means that approximately 31% of the variance in suicide/self-mutilation can be explained by the predictors included in the model. The predictors encompass a wide range of variables such as emotional states (e.g., impulsivity, intense anger, emptiness), personal demographics (e.g., age, gender, marital status), and experiences with occupation-related stressors (e.g., being attacked or arrested by the occupation).\u003c/p\u003e\n\u003cp\u003eThe ANOVA results confirm that the regression model is statistically significant (F = 9.104, p \u0026lt; 0.001), indicating that the predictors collectively have a meaningful impact on the dependent variable. Among the predictors, self-image, abandonment, and quasi-psychotic status are particularly significant, with coefficients showing strong relationships with the outcome variable. Notably, the coefficient for self-image is highly significant (p \u0026lt; 0.001), suggesting that individuals with negative self-image are more likely to report higher levels of suicide/self-mutilation. Similarly, impulsiveness and abandonment also show significant relationships, highlighting the importance of emotional regulation and personal experiences in understanding the risk of self-harm.\u003c/p\u003e\n\u003cp\u003eCollinearity diagnostics indicate that multicollinearity is not a major concern, with all variance inflation factors (VIF) well below the threshold of 10. This suggests that the predictors do not excessively overlap, allowing for reliable interpretation of their individual effects on suicide/self-mutilation. Overall, the model provides valuable insights into the factors influencing self-harm behaviors, underscoring the complex interplay of emotional, demographic, and situational variables. Further research could refine these predictors and explore additional factors to enhance the predictive power and understanding of suicide/self-mutilation.\u003c/p\u003e\n\u003cp\u003eOptimal Model Fit for Predicting Suicide and Self-Mutilation: A Path Analysis Using IBM AMOS\u003c/p\u003e\n\u003cp\u003eThe strong fit of the model in Table 6 indicates a high degree of accuracy in representing the complex relationships among psychological variables, such as impulsivity, quasi-psychotic status, and suicide risk. This exceptional fit is reflected in the model\u0026rsquo;s fit indices, including a CMIN/DF ratio of .804, which is well within the acceptable range, suggesting that the model provides an excellent approximation of the observed data. The low RMR (.005) and high GFI (.999) further confirm that the residuals are minimal, and the model captures the data\u0026apos;s underlying structure effectively. The CFI and IFI, both being 1,000, indicate that the model explains the observed data as well as possible, without any improvements needed. Additionally, the RMSEA value of .000, with its associated confidence interval, reinforces that the model fits the data perfectly, suggesting no room for improvement in fit.\u003c/p\u003e\n\u003cp\u003eTable 6. Model Fit Summary Table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eDefault Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eCMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eExcellent fit (CMIN/DF = .804, p = .492)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eGood fit (low residuals)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eHigh goodness of fit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eExcellent model fit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eExcellent fit (PCLOSE = .854)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e38.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eModel comparison indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eECVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eEffective fit considering sample size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eHOELTER .05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdequate sample size for model fit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eHOELTER .01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eRobust sample size for model validity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eModel Interpretation\u003c/h3\u003e\n\u003cp\u003eThe path analysis model in Figure 1 reveals intricate relationships among psychological factors and their impact on suicide/self-mutilation tendencies. Feelings of abandonment are significantly associated with self-image, indicating that distress related to abandonment negatively impacts one\u0026rsquo;s self-image. Additionally, feelings of abandonment significantly influence impulsiveness, suggesting that higher distress related to abandonment is linked to increased impulsive behaviors. A negative self-image also contributes to higher impulsivity.\u003c/p\u003e\n\u003cp\u003eThe model further highlights that impulsivity has a strong positive effect on quasi-psychotic symptoms, and abandonment-related distress also affects quasi-psychotic symptoms. Notably, the method of transportation (public transportation, walking, or using a private car) has a negative effect on both feelings of abandonment and suicide/self-mutilation tendencies, suggesting that transportation methods subtly influence these psychological factors. Overall, the model effectively elucidates how abandonment, self-image, impulsivity, and quasi-psychotic symptoms interrelate and contribute to suicide/self-mutilation tendencies.\u003c/p\u003e\n\u003ch3\u003eTable 7. Maximum Likelihood Estimates for the Path Analysis Model\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eC.R.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eABONDT \u0026larr; Transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-2.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eSIT \u0026larr; ABONDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e14.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eImpulsivity \u0026larr; ABONDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eImpulsivity \u0026larr; SIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eQuasi-Psychotic StatusT \u0026larr; Impulsivity T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eQuasi-Psychotic StatusT \u0026larr; ABONDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e4.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eQuasi-Psychotic StatusT \u0026larr; SIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eSuicideSelfT \u0026larr; ImpulsivityT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eSuicideSelfT \u0026larr; Transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-2.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eSuicideSelfT \u0026larr; QuasiPsychoticStatusT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eSuicideSelfT \u0026larr; SIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e4.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eSuicideSelfT \u0026larr; ABONDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e4.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7 presents key mediators in the model, including impulsivity, quasi-psychotic symptoms, and self-image. Impulsivity mediates the relationship between feelings of abandonment and quasi-psychotic symptoms, with heightened impulsivity resulting from increased feelings of abandonment, which subsequently intensifies quasi-psychotic symptoms. Quasi-psychotic symptoms also mediate the relationship between impulsivity and suicide/self-mutilation tendencies. Additionally, self-image impacts both impulsive and quasi-psychotic symptoms, thereby influencing suicide/self-mutilation tendencies. These mediators elucidate the complex psychological mechanisms at play, highlighting how emotional and cognitive factors interact to affect suicide/self-mutilation outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study contributes to a growing body of evidence on the prevalence and psychological correlations of borderline personality disorder (BPD) symptoms among populations exposed to chronic sociopolitical adversity. The findings reveal that emotional dysregulation, abandonment fears, and self-harming behaviors are significantly associated with demographic factors, medication use, and exposure to sociopolitical violence. These results are broadly consistent with previous clinical and empirical studies in conflict-affected contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender and Emotional Dysregulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data confirmed that female participants reported significantly higher levels of affective instability, abandonment concerns, and intense anger. These findings align with previous research that suggests females are more likely to exhibit internalizing symptoms of BPD and report greater emotional vulnerability compared to males (Sansone \u0026amp; Sansone, 2011; Bozzatello et al., 2024). This gender disparity may stem from both biological sensitivity and greater exposure to interpersonal stressors, as well as diagnostic biases that underreport BPD in men (Jane et al., 2007). These findings underscore the need for gender-sensitive screening tools and interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociopolitical Violence and BPD Symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants exposed to occupation-related trauma (e.g., attacks, arrests, or proximity to settlements) demonstrated significantly higher scores in abandonment, suicidal ideation, and quasi-psychotic symptoms. These results reinforce the role of external stressors in the onset and exacerbation of BPD symptoms, consistent with literature linking trauma exposure and emotional instability in high-conflict regions (Sousa, 2013; Dubow et al., 2009). The unique Palestinian sociopolitical context\u0026mdash;characterized by displacement, restricted mobility, and chronic insecurity\u0026mdash;amplifies emotional vulnerability and justifies the integration of trauma-informed approaches in treatment programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychological Predictors of Self-Harm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate regression and path analysis identified poor self-image, feelings of abandonment, quasi-psychotic symptoms, and impulsivity as significant predictors of suicide/self-mutilation behaviors. These results are congruent with prior studies demonstrating that disturbances in identity and cognition are central to self-harming tendencies in BPD patients (Suarez \u0026amp; Feixas, 2020). Although impulsiveness had a weaker predictive value than anticipated, it served as a key mediator in the pathways from abandonment and self-image to quasi-psychotic symptoms and suicidality.\u003c/p\u003e\n\u003cp\u003eThis complexity supports the view that interventions targeting impulsive alone may not suffice; instead, programs should also address cognitive conflict and self-schema disturbances that fuel emotional instability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedication Use and Unresolved Distress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA noteworthy and somewhat paradoxical finding was that participants who used medication for mood or stress reported higher levels of psychological distress across all domains. This may reflect a treatment bias, where individuals with more severe symptoms are more likely to be prescribed medications. Alternatively, it may indicate limited effectiveness of pharmacotherapy when not integrated with psychosocial interventions echoed by Paton et al. (2015). These results highlight the necessity of combining medication with evidence-based psychotherapies such as DBT or CBT.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRural Residency and Limited Access to Care\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants residing in rural areas showed higher levels of abandonment fears, suicidal ideation, and quasi-psychotic symptoms compared to urban residents. This supports previous research indicating that structural limitations in rural mental health services exacerbate emotional distress (L\u0026ouml;tzer, 2023). Culturally sensitive, community-based mental health interventions are urgently needed in these regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransportation and Socioeconomic Disadvantage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn unexpected yet important finding was the association between transportation methods and psychological symptoms. Individuals relying on public transportation or walking reported greater emotional distress than those using private cars. This suggests that transportation may be a proxy for broader socioeconomic hardship, with longer commutes and lower mobility reflecting chronic daily stress. This novel result invites further research into how seemingly peripheral socioeconomic factors influence mental health in conflict zones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Networks and Therapeutic Potential\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough not a primary focus, the results indirectly support Beeney et al.\u0026rsquo;s (2018) findings on the role of social disadvantage and interpersonal isolation in BPD symptomatology. In the Palestinian context, where sociopolitical trauma restricts social cohesion, strengthening interpersonal support systems may mitigate symptoms and improve therapy outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for Clinical Practice and Policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026rsquo;s results highlight the importance of addressing the psychological consequences of sociopolitical trauma through contextually adapted mental health policies. Training healthcare professionals to recognize and respond empathetically to BPD symptoms\u0026mdash;especially in marginalized populations\u0026mdash;is vital, as stigma among providers can undermine treatment effectiveness (Aljohani et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be noted, including the use of non-parametric tests due to non-normal data may limit generalizability. The cross-sectional design prevents causal inference. The sample skew toward single, medical students may limit population-wide applicability.\u003c/p\u003e\n\u003cp\u003eFuture studies should adopt longitudinal designs, expand demographic representation, and explore interventions that integrate trauma-informed, gender-sensitive, and culturally grounded approaches.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides robust evidence on the psychological, demographic, and contextual determinants of borderline personality disorder (BPD) symptoms among Palestinian university students. It demonstrates that females, individuals exposed to sociopolitical violence, and those residing in rural areas or using psychiatric medication reported higher levels of affective instability, abandonment, and self-harming tendencies.\u003c/p\u003e\n\u003cp\u003eKey psychological predictors of self-harm included disturbed self-image, impulsivity, and quasi-psychotic symptoms, emphasizing the urgent need for integrated interventions that target both emotional regulation and cognitive conflicts. The findings also uncovered the significant yet underexplored role of socioeconomic indicators, such as transportation methods, as stressors linked to mental health.\u003c/p\u003e\n\u003cp\u003eGiven the sociopolitical complexities and limited mental health infrastructure in Palestine, there is a critical need for culturally adapted, trauma-informed, and community-based mental health services. Future research should prioritize longitudinal designs to better understand symptom trajectories and the long-term effectiveness of tailored interventions in conflict-affected populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBPD: borderline personality disorder\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBPQ: borderline personality questionnaire\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMSI-BPD: McLean Screening Instrument for borderline personality disorder\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the \u003cem\u003eInstitutional Review Board (IRB) of An-Najah National University\u003c/em\u003e (approval number: Med. June 2023/16). Informed consent was obtained from all participants prior to their involvement in the study. All participants consented to the anonymous use of their data for research purposes, with the assurance that the data would only be used for clinical research and publication following the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data from the current work is obtainable from the corresponding author upon request. [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAH, MA, AS, TK, NJ, and FA contributed to the literature review, data collection, data analysis, and initial drafting of the manuscript. AAH, MA, AS, TK, and NJ were involved in study design. They also drafted the manuscript, ensured data integrity, and critically reviewed the research to enhance its intellectual content. AAH conceptualized the study, ensured data integrity, and provided critical intellectual input. He also conceived and designed the research, supervised and coordinated data analysis, AAH, and FA critically reviewed the interpretation of results, and assisted with the final manuscript preparation. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to An-Najah National University (www.najah.edu) and all participating hospitals for their invaluable support in providing resources and facilitating the research process.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChapman J, Jamil RT, Fleisher C. Borderline Personality Disorder. 2023 Jun 2. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan. \u003c/li\u003e\n\u003cli\u003eBiskin, RS. The Lifetime Course of Borderline Personality Disorder. Can J Psychiatry. 2015 Jul;60(7):303-8. doi: 10.1177/070674371506000702. PMID: 26175388; PMCID: PMC4500179.\u003c/li\u003e\n\u003cli\u003eAmerican Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596\u003c/li\u003e\n\u003cli\u003eVideler AC, Hutsebaut J, Schulkens JEM, Sobczak S, van Alphen SPJ. A Life Span Perspective on Borderline Personality Disorder. Curr Psychiatry Rep. 2019 Jun 4;21(7):51. doi: 10.1007/s11920-019-1040-1. PMID: 31161404; PMCID: PMC6546651.\u003c/li\u003e\n\u003cli\u003eBozzatello P, Garbarini C, Rocca P, Bellino S. Borderline Personality Disorder: Risk Factors and Early Detection. Diagnostics (Basel). 2021 Nov 18;11(11):2142. doi: 10.3390/diagnostics11112142. PMID: 34829488; PMCID: PMC8620075.\u003c/li\u003e\n\u003cli\u003eCohen P., Chen H., Gordon K., Johnson J., Brook J., Kasen S. Socioeconomic background and the developmental course of schizotypal and borderline personality disorder symptoms. Dev. Psychopathol. 2008;20:633\u0026ndash;650. doi: 10.1017/S095457940800031X\u003c/li\u003e\n\u003cli\u003eCrawford T.N., Cohen P.R., Chen H., Anglin D.M., Ehrensaft M. Early maternal separation and the trajectory of borderline personality disorder symptoms. Dev. Psychopathol. 2009;21:1013\u0026ndash;1030. doi: 10.1017/S0954579409000546\u003c/li\u003e\n\u003cli\u003eParis J. Personality disorders over time: precursors, course and outcome. J Pers Disord. 2003 Dec;17(6):479-88. doi: 10.1521/pedi.17.6.479.25360. PMID: 14744074.\u003c/li\u003e\n\u003cli\u003eSteele K.R., Townsend M.L., Grenyer B.F.S. Parenting and personality disorder: An overview and meta-synthesis of systematic reviews. PLoS ONE. 2019;14:e0223038. doi: 10.1371/journal.pone.0223038.\u003c/li\u003e\n\u003cli\u003eVanwoerden S., Kalpakci A., Sharp C. The relations between inadequate parent-child boundaries and borderline personality disorder inadolescence. Psychiatry Res. 2017;257:462\u0026ndash;471. doi: 10.1016/j.psychres.2017.08.015\u003c/li\u003e\n\u003cli\u003eLyons-Ruth K., Bureau J.-F., Holmes B., Easterbrooks A., Brooks N.H. Borderline symptoms and suicidality/self-injury in late adolescence: Prospectively observed relationship correlates in infancy and childhood. Psychiatry Res. 2013;206:273\u0026ndash;281. doi: 10.1016/j.psychres.2012.09.030.\u003c/li\u003e\n\u003cli\u003eBozzatello P, Blua C, Brandellero D, Baldassarri L, Brasso C, Rocca P, Bellino S. Gender differences in borderline personality disorder: a narrative review. Front Psychiatry. 2024 Jan 12;15:1320546. doi: 10.3389/fpsyt.2024.1320546. PMID: 38283847; PMCID: PMC10811047.\u003c/li\u003e\n\u003cli\u003eJane JS, Oltmanns TF, South SC, Turkheimer E. Gender bias in diagnostic criteria for personality disorders: an item response theory analysis. J Abnorm Psychol. (2007) 116:166\u0026ndash;75. doi: 10.1037/0021-843X.116.1.166\u003c/li\u003e\n\u003cli\u003eSansone RA, Sansone LA. Gender patterns in borderline personality disorder. Innov Clin Neurosci. 2011 May;8(5):16-20. PMID: 21686143; PMCID: PMC3115767.\u003c/li\u003e\n\u003cli\u003eTedstone Doherty D, Kartalova-O\u0026apos;Doherty Y. Gender and self-reported mental health problems: predictors of help seeking from a general practitioner. Br J Health Psychol. 2010 Feb;15(Pt 1):213-28. doi: 10.1348/135910709X457423. Epub 2009 Jun 12. PMID: 19527564; PMCID: PMC2845878.\u003c/li\u003e\n\u003cli\u003eLenzenweger MF, Lane MC, Loranger AW, Kessler RC. DSM-IV personality disorders in the National Comorbidity Survey Replication. Biol Psychiatry. 2007 Sep 15;62(6):553-64. doi: 10.1016/j.biopsych.2006.09.019. Epub 2007 Jan 9. PMID: 17217923; PMCID: PMC2044500.\u003c/li\u003e\n\u003cli\u003eChanen, A.M., Nicol, K., Betts, J.K. et al. INdividual Vocational and Educational Support Trial (INVEST) for young people with borderline personality disorder: study protocol for a randomised controlled trial. Trials 21, 583 (2020). https://doi.org/10.1186/s13063-020-04471-3\u003c/li\u003e\n\u003cli\u003eHastrup, L.H., Jennum, P., Ibsen, R. et al. Welfare consequences of early-onset Borderline Personality Disorder: a nationwide register-based case-control study. Eur Child Adolesc Psychiatry 31, 253\u0026ndash;260 (2022). https://doi.org/10.1007/s00787-020-01683-5\u003c/li\u003e\n\u003cli\u003ePoreh AM, R. D. (2006). The BPQ: A scale for the assessment of borderline personality based on DSM-IV. guilford journals, Volume 20.\u003c/li\u003e\n\u003cli\u003eZanarini, M. C. (2003). A Screening Measure for BPD: The McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD). . guilford journals, Volume 17.\u003c/li\u003e\n\u003cli\u003eSansone, Randy A, and Lori A Sansone. \u0026ldquo;Gender Patterns in Borderline Personality Disorder.\u0026rdquo; Innovations in Clinical Neuroscience, vol. 8, no. 5, May 2011, p. 16, pmc.ncbi.nlm.nih.gov/articles/PMC3115767/. \u003c/li\u003e\n\u003cli\u003e\u0026ldquo;PSV va News - Summer 2018 - Are There Gender Differences in Borderline Personality Disorder?\u0026rdquo; Psva.org, psva.org/wp-content/uploads/2018/summer/news2.html.\u003c/li\u003e\n\u003cli\u003eSousa, Cindy A. \u0026ldquo;Political Violence, Collective Functioning and Health: A Review of the Literature.\u0026rdquo; Medicine, Conflict and Survival, vol. 29, no. 3, Sept. 2013, pp. 169\u0026ndash;197, https://doi.org/10.1080/13623699.2013.813109. \u003c/li\u003e\n\u003cli\u003eDubow, Eric F., et al. \u0026ldquo;Exposure to Conflict and Violence across Contexts: Relations to Adjustment among Palestinian Children.\u0026rdquo; Journal of Clinical Child \u0026amp; Adolescent Psychology, vol. 39, no. 1, 31 Dec. 2009, pp. 103\u0026ndash;116, https://doi.org/10.1080/15374410903401153 . Accessed 22 Mar. 2019. \u003c/li\u003e\n\u003cli\u003eSuarez, Victor, and Guillem Feixas. \u0026ldquo;Cognitive Conflict in Borderline Personality Disorder: A Study Protocol.\u0026rdquo; Behavioral Sciences, vol. 10, no. 12, 26 Nov. 2020, p. 180, https://doi.org/10.3390/bs10120180. \u003c/li\u003e\n\u003cli\u003ePaton, Carol, et al. \u0026ldquo;The Use of Psychotropic Medication in Patients with Emotionally Unstable Personality Disorder under the Care of UK Mental Health Services.\u0026rdquo; The Journal of Clinical Psychiatry, vol. 76, no. 04, 22 Apr. 2015, pp. e512\u0026ndash;e518, https://doi.org/10.4088/jcp.14m09228. \u003c/li\u003e\n\u003cli\u003eL\u0026ouml;tzer, Dorian. \u0026ldquo;Mental Health in the West Bank and Gaza \u0026ndash; ISDC.\u0026rdquo; ISDC, 10 Feb. 2023, isdc.org/publications/mental-health-in-the-west-bank-and-gaza/. Accessed 27 Feb. 2025.\u003c/li\u003e\n\u003cli\u003e\u0026ldquo;Findings by Dr. Joseph E. Beeney Show That Individuals with Borderline Personality Disorder Are at a Social Disadvantage.\u0026rdquo; University of Pittsburgh Department of Psychiatry, 2018, www.psychiatry.pitt.edu/findings-dr-joseph-e-beeney-show-individuals-borderline-personality-disorder-are-social. Accessed 27 Feb. 2025. \u003c/li\u003e\n\u003cli\u003eBeeney, Joseph E., et al. \u0026ldquo;Social Disadvantage and Borderline Personality Disorder: A Study of Social Networks.\u0026rdquo; Personality Disorders: Theory, Research, and Treatment, vol. 9, no. 1, Jan. 2018, pp. 62\u0026ndash;72, www.ncbi.nlm.nih.gov/pmc/articles/PMC5468502/, https://doi.org/10.1037/per0000234.\u003c/li\u003e\n\u003cli\u003eAljohani, Enas M, et al. \u0026ldquo;Mental Health Workers\u0026rsquo; Knowledge and Attitude towards Borderline Personality Disorder: A Saudi Multicenter Study.\u0026rdquo; Cureus, vol. 14, no. 11, 27 Nov. 2022, https://doi.org/10.7759/cureus.31938.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Borderline Personality Disorder, Sociopolitical Violence, Self-Harm, Emotional Dysregulation, Trauma","lastPublishedDoi":"10.21203/rs.3.rs-6746037/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6746037/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Borderline Personality Disorder (BPD) is a severe mental health condition that is often intensified by trauma, instability, and sociocultural stressors. This study investigates the prevalence and psychological predictors of BPD symptoms among Palestinian university students in a politically unstable context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional study was conducted among 538 participants across the West Bank using validated Arabic versions of the McLean Screening Instrument and the Borderline Personality Questionnaire. Statistical analyses included Spearman’s correlation, Mann-Whitney, Kruskal-Wallis, linear regression, and path analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Females reported significantly higher levels of affective instability, abandonment fears, and intense anger. Exposure to sociopolitical violence (e.g., living near settlements or being attacked) was associated with elevated BPD symptoms. Regression and path analysis identified disturbed self-image, abandonment, impulsivity, and quasi-psychotic symptoms as key predictors of self-harm. Unexpectedly, transportation methods and medication use were also associated with symptom severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Sociopolitical adversity, gender, and psychological vulnerabilities significantly influence BPD symptom expression in Palestinian youth. These findings highlight the urgent need for culturally tailored, trauma-informed, and gender-sensitive mental health interventions in conflict zones. Future longitudinal studies are recommended to monitor symptom development and evaluate the effectiveness of targeted therapies.\u003c/p\u003e","manuscriptTitle":"Borderline Personality Disorder Symptoms in a Conflict-Affected Population: A Study Among University Students in a Low-Middle-Income Country","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 11:36:09","doi":"10.21203/rs.3.rs-6746037/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"83212709863433187785396726775882867305","date":"2025-07-13T11:50:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-04T11:30:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-09T16:35:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-06T15:03:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-06T15:01:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-05-26T00:41:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0d31cb9-4d35-4fe2-8fb4-312325be1240","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-09T11:36:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 11:36:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6746037","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6746037","identity":"rs-6746037","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}