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Article

Borderline Personality Symptoms: What Not to Be Overlooked When Approaching Suicidal Ideation among University Students

by
Nahathai Wongpakaran
1,
Awirut Oon-Arom
1,
Nuntaporn Karawekpanyawong
1,
Trustsavin Lohanan
2,
Thanakorn Leesawat
2 and
Tinakon Wongpakaran
1,*
1
Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
2
Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
*
Author to whom correspondence should be addressed.
Healthcare 2021, 9(10), 1399; https://doi.org/10.3390/healthcare9101399
Submission received: 30 July 2021 / Revised: 29 September 2021 / Accepted: 18 October 2021 / Published: 19 October 2021
Browse Figure
Review Reports Versions Notes

Abstract

:
Suicidal ideation is a serious condition antecedent to suicidal attempts and is highly related not only to depression but also other psychosocial factors. This study aimed to examine the predictive effects of these potential factors for suicidal ideation among young adult university students. A cross-sectional survey was conducted on a sample of university students in Thailand. An online questionnaire employed the perceived stress scale-10 (PSS-10), the patient health questionnaire-8 (PHQ-8), the multidimensional scale of perceived social support (MSPSS), and a screening instrument for borderline personality disorder. An ordinal regression analysis was applied to determine the predictive effects of the independent variables. Of 336 students, the mean age was 20.26 ± 1.3 years, 80.4% of whom were female; 14.3% had suicidal ideation. The significant predictors of suicidal thoughts were perceived stress (AOR 1.11, 95% CI 1.01 to 1.22); depressive symptoms (AOR 1.16, 95% CI 1.05 to 1.22); borderline personality symptoms (AOR 1.19, 95% CI 1.01 to 1.40); and perceived social support (AOR 0.97, 95% CI 0.94 to 1.00). Not only did depressive symptoms contribute to suicidal ideation but they also constituted important variables. Therefore, they should be included in intervention plans to prevent suicidality among university students.

1. Introduction

In the transition from adolescence to adulthood, university students often are required to adapt to new social roles, to deal with their own finances, and to strive for academic achievement [1]. Some may undergo a mental health hurdle such as stress, amotivation, interpersonal difficulty, or depression [2,3,4]. The mental health problems of university students have strikingly increased in the past decade [5,6], especially among medical students. A systematic review has reported that the prevalence of depression among university students ranged from 10 to 85% [7].
One serious element of psychological distress is the suicide attempt, for which empirically, the most potent predictor is suicidal ideation [8]. A study in the US found that 24% of undergraduate subjects had suicidal ideation and 9% had attempted suicide [5], compared with a 9% prevalence of suicidal ideation, and 2.7% of suicide attempts among adults worldwide [9]. Suicidal ideation is omnipresent among university students across culture and religion. A survey among Muslim university students showed that 22% of the participants reported suicidal ideation and 8.6% reported attempting suicide [10]. In some countries, studies showed that the prevalence of suicidal ideation and attempt were 23.7 and 3.9%, respectively [11]. In Thailand, the prevalence of suicidal ideation in the past 12 months was 8.8% (9.9% among males and 7.7% among females) [12].
In addition to sociodemographic variables, e.g., being female [6], many factors are associated with suicidal ideation, especially psychosocial variables that typically accompany this developmental period [13]. Studies have verified that university students experience high levels of stress [2,14], which are strongly associated with a greater likelihood of suicidality [5]. Sources of stress included academic, family-related, and relationship-related causes. One half of students reported the presence of academic stress as an important life stressor leading to suicidality [15].
Depression may be the most common variable related to both suicidalities, either suicidal ideation or suicide attempt [6,16,17,18]. Related studies have reported a wide range of the prevalence of depression among university students, from 10 to 85% [7]. Similar to studies in Western countries, depression constituted a significant predictor of high suicide attempt risk as indicated in Asian countries [19,20]. The incidence strikingly increased during the COVID-19 outbreak. A study showed that during lockdown, major depression and suicidal attempts were present among 12.43 with 13.46% of subjects experiencing severe distress [21,22].
In addition to stress and depression, perceived social support, a positive factor, was found to be negatively associated with suicidal ideation. Many studies have revealed that the odds of feeling poor social support involved two to five times increased odds of suicide ideation [6,11]. Individuals with feelings of poor social support had two to five times the odds of suicidal ideation than those who had well-perceived social support. This finding was consistent between Western and Eastern cultures [19,23,24,25,26].
One important factor, related to suicidal ideation, is childhood history of abuse and maltreatment [16,27,28,29,30]. This factor is fundamentally associated with borderline personality disorder (BPD) in older age [31]. The core features of borderline personality include unstable interpersonal relationships, poor self-image, and affects, and marked impulsivity [32], which usually become apparent during adolescence and young adulthood [33,34].
A review of 43 studies discovered that the prevalence of BPD among university students was 0.5 to 32.1%, with an unadjusted lifetime prevalence of 9.7% [35]. One of the most singular aspects of BPD among college students was chronic suicidal ideation due to being associated with poor self-concept and identity [36,37,38], and was predictive of a forthcoming suicide attempt [39,40].
In addition to suicidality, BPD is closely related to high levels of perceived stress, depression, and a low level of perceived social support [40,41,42,43,44].
As the rate of increasing suicidal ideation and attempts has become a major concern among university students, many studies have focused on suicidal ideation, most emphasizing depression, but insufficiently on psychosocial issues, i.e., perceived stress and perceived social support that is deemed modifiable [45,46]. Many studies have indirectly investigated personality factors, including several in Thailand, but without exploring borderline personality directly [12,47]. Due to adapting to a transitional stage of life and encountering environmental challenges, some university students with certain personality types may experience tremendous distress; therefore, including the personality factor would be important when studying suicidality among university students. More importantly, to our knowledge, the modifiable positive and negative psychosocial factors have not been analyzed concurrently with borderline personality among university students. Therefore, this study aimed to explore these potential predictors related to suicidal ideation. We hypothesized that all of these psychosocial variables may constitute significant predictors for suicidal ideation among Thai university students.

2. Materials and Methods

This study employed a cross-sectional online survey of 336 undergraduate students in Thailand in late 2019 before the COVID-19 pandemic in Thailand. It comprised a cross-sectional online survey. The study was approved by the Ethics Committee of the Faculty of Medicine at Chiang Mai University, Thailand.
  • Participants
Undergraduate students throughout Thailand were invited to participate in the study. The inclusion criteria included age 18 to 25 years, studying at an undergraduate level, fluent in writing and speaking Thai, and able to access the Internet. The exclusion criteria included being diagnosed with schizophrenia, bipolar disorder, drug or alcohol use disorder, and being intoxicated with alcohol within 24 h before participating in the study. After finalizing a written informed consent form, the participants were asked to complete questionnaires on the Internet via personal computer, laptop, smartphone, or tablet. The amount of 100 THB (3 USD) was given to each participant for compensation after they had submitted their answers.
The target sample size of participants was estimated based on related studies among university students. As reviewed, the range of the prevalence of suicidal ideation, depression, and borderline personality were rather wide; we determined the prevalence of 30% for calculating the sample size. The minimum number of the sample size was 323, required to complete the online survey to ensure a power of 80% and a 0.05 type I error. The number of responses totaled 355. Among them, 13 were excluded: 5 respondents were older than the inclusion criteria (25 years old) and 8 comprised repeat responses. The final number of participants was 342. Of the 342 participants, 336 (98.25%) completed the questionnaires and their data were used for analysis.
  • Instruments
In addition to the sociodemographic data, e.g., age, sex, number of years of studying, income, etc., the participants were asked to complete the following measurements.
  • Screening Instrument for Borderline Personality Disorder (SI-Bord)
The SI-Bord consists of 5 questions addressing the salient criteria of DSM-5 of BPD for university students but was modified based on the Thai cultural context [48]. It includes (i) abandonment avoidance; (ii) interpersonal relationships instability; (iii) identity disturbance; (iv) suicidal and self-harm behaviors; and (v) affective instability. The SI-Bord uses a 4-point Likert scale, ranging from never (0) to very often (3). Samples of the questionnaires include “My feelings suddenly changed, such as ‘I don’t know who I am,’ ‘I don’t know where I am going’, ‘I feel lonely deep down’ or ‘I have no goal in life’”. The total score ranges from 0 to 15 and higher scores represent more BPD symptoms (BPS). The study sample yielded a Cronbach’s alpha of 0.76.
2.
Revised Thai Multidimensional Scales of Perceived Social Support (r-MSPSS)
This instrument measures the extent to which an individual has experienced being supported by family members, friends, and special individuals [49]. It includes 12 questions using a 7-point Likert scale ranging from very strongly disagree (0) to very strongly agree (6). The higher the score, the higher the level of perceived social support is attained. The revised Thai version demonstrates good psychometric properties [50]. The study sample yielded a Cronbach’s alpha of 0.91.
3.
Thai Version of Perceived Stress Scales (T-PSS-10)
This instrument measures the extent to which an individual perceived stress over the past four weeks. It comprises 10 questions with a 5-point Likert scale ranging from never (0) to very often (4). The higher the total score, the higher the level of feeling stress is attained. The T-PSS-10 demonstrates good psychometric properties [51]. The study sample yielded a Cronbach’s alpha of 0.85.
4.
Patient-Health Questionaire-8 (PHQ-8)
This instrument measures the extent to which an individual has experienced depressive symptoms over the past two weeks [52,53]. The PHQ-8 differs from the more commonly used PHQ-9 in that it removes the last question addressing suicidal ideation. It contains 8 questions with a 4-point Likert scale ranging from 0 (not at all) to 3 (nearly every day). The higher the total score, the higher the level of depressive symptoms is attained. The study sample yielded a Cronbach’s alpha of 0.89.
5.
Suicidality Ideation
The item of suicidal ideation was drawn from the Patient-Health Questionaire-9 (PHQ-9). This item asks the respondent to rate how much s/he feels about “Thoughts that you would be better off dead, or thoughts of hurting yourself in some way?” The 4-response Likert scale ranges from response options 0 (not at all) to 3 (nearly every day). Because it comprises a single item, no internal consistency was provided.
  • Statistical Analysis
To test the hypothesis, we used a regression analysis. As the outcome, suicidal ideation, was ordinal, ordinal regression or an ordinal logistic regression analysis was applied instead of binary logistic regression to maintain information about the ordering.
Ordinal regression is used to predict an ordinal dependent variable given one or more independent variables. We used ordinal regression to predict the suicidal ideation, measured on a 4-point Likert item from “not at all” to “nearly every day”, based on four independent variables: depressive symptoms (PHQ-8), borderline personality symptoms (SI-Bord), and perceived stress (PSS), and perceived social support (r-MSPSS). In this case, we used ordinal regression to determine whether these independent variables predict the ordinal dependent variable, “suicidal ideation”, where suicidal ideation was determined by categorizing the suicide items in three levels; 0 = no suicidal ideation, 1 = mild, and 2 = moderate to severe (calculated by combining scores 2 and 3). As the independent variables were continuous, we interpreted how a single unit increase or decrease in that variable (e.g., a score increase or decrease in the SI-Bord score), was associated with the odds of suicidal ideation having a higher or lower value (e.g., a one score increase in participants’ SI-Bord score increases the odds that they would consider suicidal ideation to be at a higher level).
Before we began looking at the effects of each explanatory (independent) variable in the model, we had to consider whether the model improved the ability to predict the outcome. To do that, we compared the baseline or ‘Intercept Only’ model (a model without any explanatory variables) against the ‘Final’ model (the model with all the explanatory variables) to see whether it had significantly improved the fit to the data.
The model fitting information containing the −2 Log likelihood for an intercept only (or null) model and the final model (containing the full set of predictors) was analyzed. A likelihood ratio chi-square test was used to test whether significant improvement was indicated in the fit of the final model relative to the intercept only model.
“Goodness of Fit” was illustrated to determine whether a model exhibited good fit to the data. Nonsignificant test results were indicators that the model fit the data well (Field, 2018; Petrucci, 2009). Pseudo-R-square values, unlike the R-square value in the ordinary least square regression, were used in the model fit. McFadden pseudo-R-square values 0.2–0.4 were considered a good model fit [54]. The regression coefficients were used for the predicted change in log odds of being in a higher (as opposed to a lower) group/category on the suicidal ideation level (controlling for the remaining independent variables) per unit increase on the predictors. Odds ratios were also calculated based on regression coefficients, reflecting the multiplicative change in the odds of being in a higher level of suicidal ideation for every unit increase on each particular predictor, holding the remaining predictors constant. An odds ratio > 1 suggested an increasing probability of being in a higher level concerning suicidal ideation as values on a predicting variable increased, whereas a ratio < 1 suggested a decreasing probability with increasing values regarding a predicting variable. An odds ratio = 1 suggested no predicted change in the likelihood of being in a higher category as values on a predicting variable increased.
For sociodemographic data such as sex, year of education, and total scores of psychological variables such as SI-bord score and PSS score, descriptive statistics, i.e., frequency, percentage, mean, and SD were used. A significance level at p < 0.05 was considered acceptable. All analyses were performed using IBM SPSS (manufacturer, city and country), Version 22. MedCalc (manufacturer, city and country), Version 19.7 (MedCalc Software, Mariakerke, Belgium) was used to create the graphs.

3. Results

Of 336 participants, 275 were females (80.4%) with a mean age of 20.25. (SD = 1.4). The majority of participants comprised health science students. Among all, 14.3% had suicidal ideation. The median score of suicidal ideation was 0, and the interquartile range was 0. Other characteristics are shown in Table 1.
Table 2 shows the difference among variables based on the level of suicidal ideation. Sociodemographic variables included the number of years studying, and the higher risk for suicidal ideation (p < 0.05). Non-health science students reported higher levels of suicidal ideation than health science students (p < 0.001). For psychological variables, the scores of the SI-Bord, TPSS, and PHQ-8 were higher in higher levels of suicidal ideation, and vice versa for the r-MSPSS (all p < 0.001) (Figure 1).
We further conducted a univariate regression analysis for each variable to determine what variables should be included in the multiple ordinal regression model. Finally, we selected age, number of years studying, academic major, as well as the total scores of SI-Bord, r-MSPSS, TPSS, and PHQ-8.
Table 3 shows the univariable regression analysis results. All models fit the data well indicated by the nonsignificance of the Pearson chi-square test and the deviance test results. The standardized regression coefficients ranged from 0.086 to 1.299. All except r-MSPSS exhibited positive associations with suicidal ideation.
For the multivariable regression analysis as shown in Table 4, the model fitting information using a likelihood ratio chi-square test revealed a significantly improved fit of the final model relative to the intercept only (null) model (χ2 (6) = 127.66, p < 0.001). Then the “Goodness of Fit” was confirmed by the nonsignificance of the Pearson chi-square test (χ2 (663) = 409.82, p = 1.000) and the deviance test (χ2 (664) = 207.57, p = 1.000). Pseudo-R-square values were as follows: Cox and Snell = 0.316, Nagelkerke = 0.501, McFadden = 0.381, also indicating that the model displayed a good fit.
The regression coefficients were interpreted as the predicted change in log odds of being in a higher category concerning the suicidal ideation variable (controlling for the remaining predicting variables) per unit increase on the predicting variables. All, except r-MSPSS, were significant positive predictors of the presence of suicidal ideation. PHQ-8 demonstrated a coefficient of 0.149, denoting a predicted increase of 0.149 in the log odds of a student being in a higher category concerning suicidal ideation. In other words, an increase in depressive symptoms was associated with an increase in the odds of suicidal ideation, with an odds ratio of 1.16 (95% CI, 1.05 to 1.22), Wald χ2 (1) = 7.80, p < 0.01. The same was true for TPSS (Wald χ2 (1) = 5.297, p < 0.05), SI-Bord (Wald χ2 (1) = 4.476, p < 0.05), and r-MSPSS scores (Wald χ2 (1) = 4.575, p < 0.05). For r-MSPSS, an increase in r-MSPSS scores was associated with a decrease in the odds of suicidal ideation, with an odds ratio of 0.97 (95% CI, 0.94 to 1.00).
Among all predictors, SI-Bord scores showed the highest effect size. Age, number of years of studying, and academic major became nonsignificant predictors in the model.

4. Discussion

This study aimed to examine the relevant psychosocial variables as predictors for suicidal ideation among these young adults. The findings support related studies, particularly, the role of BPS feelings of stress, and of support concerning suicidal ideation [36]. This sheds some light on the role of BPS on suicidal ideation, in that both depressive symptoms and BPS were highly, significantly associated with suicidal ideation. Consistent with a related study, BPS appeared to have a stronger effect size than depressive symptoms [55]. In other words, even without depressive symptoms, suicidal ideation remained due to BPS. This was conceivably understood because borderline personality usually involves low self-esteem and suicidality. Some characteristics reverberating intra- and interpersonal difficulties, among them feelings of emptiness and interpersonal relationship, can lead to suicidal ideation or even attempts.
Some may argue that suicidality is one part of depression. This is absolutely true as it could be found in high levels of depression based on symptom hierarchy [56]. However, for those exhibiting borderline personality, suicidality may be present in a milder form of depression [57,58]. Many studies did not exclude the suicidal item from the whole depressive questionnaire, rendering a falsely inflated correlation between depressive symptoms and suicidal ideation. That is why we used the PHQ-8, a depression measurement without suicidality, in this study to ensure that the duplication bias was removed and allowing us to observe the true effect size of depressive symptoms on suicidal ideation. Taking all variables together, our findings conveyed an important message that suicidal ideation among university students should be screened for BPS, exclusively from depressive symptoms.
In line with related studies, younger age was associated more with suicidal thought [59], which could have been contributed to the fact that these students had less experience and needed more time for adjustments. The fact that the higher level of studying meant a higher probability to have suicidal ideation may have contributed to an exposure to more stress, especially among health science students [60,61]. Some investigators have suggested that higher education levels may be related to burnout and depression, leading to suicidal ideation, especially among medical and nursing students [1,62,63,64]. However, in this study, age, years of study, and the study major were overshadowed by psychosocial variables. Moreover, none of these mentioned studies included BPS in their studies, and some investigators assumed that suicidal ideation was only one part of depression.
Notably, perceived social support, the only positive psychosocial variable, was shown to provide a protective effect. Growing evidence supports that perceived social support exists among university students with mental health problems. During the COVID-19 pandemic, a high prevalence of mental health issues was observed among students undergoing quarantine. A study with a large sample size revealed that the prevalence of suicidal thoughts was 11.4%, a high level of perceived stress was 4.7%, and severe depression was 16.1% [65]. Another study among university students conducted in three Asian countries including Thailand [66] found a correlation between perceived support and suicidal thoughts among Indonesians and Thais, but not Taiwanese [66]. However, a recent study excluded BPS from their investigation [66]. We assumed, especially in the latter study, that BPS would come into play when different culture/countries become an issue as the personality factor varies from country to country [67].
Our study demonstrated that the model comprising important factors related specifically to university students, especially BPS, appears to be omnipresent in the present day. Suicidal ideation is a small and known part of the larger part, depression, evoked by the imbalance between stressor and social support, whereas most underlying parts might comprise borderline personality. To tackle suicidality, these important variables should be identified in a survey or screening process. Further, providing social support and reducing stress are essential methods to reduce depression and suicidal ideation. Strategic plans should be implemented to help mitigate BPS as well as to promote positive strengths, especially during the early years of university life. Dealing with personality issues would present a tangible challenge, but to protect the student’s wellbeing, this mission may be unavoidable. Overall, our research emphasizes and highlights not overlooking borderline personality disorder in these populations and would like to create the clinicians’ or healthcare providers’ awareness to identify such problems before any intervention. In fact, there have already been many interventions as well as numerous screening tools for borderline personality. What our research adds here is to remind and demonstrate the importance of borderline personality among these young students.
  • Strengths and Limitations
This study is, to the best of our knowledge, one of the early research reports exploring the relationship between suicidal ideation and variables specific to young adult university students, i.e., BPD symptoms, perceived stress, depressive symptoms, and perceived social support.
This study encountered several limitations. First, the single item suicidality measurement may pose some risk in reliability. A more reliable multiple item measurement for evaluating suicidality is encouraged for further studies. Second, we used the overall, but not specific, type of perceived social support, i.e., family members, friends, and special individuals. As this does not render an opportunity to examine the influence of each type of social support on this age group, a separate analysis should be considered in further studies. Third, social desirability bias may be inevitably present in such self-reporting.

5. Conclusions

These findings of suicidal ideation among university students underscore the need for covering the relevant psychosocial factors. Although depression is a major contributor to suicidal ideation, our findings showed that borderline personality symptoms could at least equally be held accountable. The associated variables, i.e., high level of perceived stress, and poor social support were also potent predictors and should not be overlooked. They should be included in further interventions and prevention plans regarding suicidality among university students.

Author Contributions

Conceptualization, N.W., N.K., A.O.-A. and T.L. (Trustsavin Lohanan), T.L. (Thanakorn Leesawat) and T.W.; methodology, T.W. and N.W.; software, T.W. and N.W.; validation, N.K., A.O.-A., T.L. (Trustsavin Lohanan) and T.L. (Thanakorn Leesawat); formal analysis, N.W. and T.W.; investigation, N.K. and A.O.-A.; resources, N.W., T.L. (Trustsavin Lohanan) and T.L. (Thanakorn Leesawat); data curation, T.L. (Trustsavin Lohanan) and T.L. (Thanakorn Leesawat); writing—original draft preparation, all; writing—review and editing, all; visualization, N.K. and T.W.; supervision, N.W.; project administration, N.W., T.L. (Trustsavin Lohanan) and T.L. (Thanakorn Leesawat); funding acquisition, T.W., T.L. (Trustsavin Lohanan) and T.L. (Thanakorn Leesawat). All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Faculty of Medicine Research Fund of Chiang Mai University (Grant no. 032/2563). The funders had no role in the study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the research ethics committee of the Faculty of Medicine, Chiang Mai University (protocol code PSY 2562-06692 and date of approval: 19 October 2019).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the subjects to publish this paper.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest. The funders played no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Maser, B.; Danilewitz, M.; Guérin, E.; Findlay, L.; Frank, E. Medical Student Psychological Distress and Mental Illness Relative to the General Population: A Canadian Cross-Sectional Survey. Acad. Med. 2019, 94, 1781–1791. [Google Scholar] [CrossRef]
  2. Sathirapanya, C. Hhempan W: Stress among Students in UniversityStress among Students in UniversityStress among students in university. J. Lib. Arts Maejo U. 2013, 1, 42–58. [Google Scholar]
  3. Kunanitthaworn, N.; Wongpakaran, T.; Wongpakaran, N.; Paiboonsithiwong, S.; Songtrijuck, N.; Kuntawong, P.; Wedding, D. Factors associated with motivation in medical education: A path analysis. BMC Med. Educ. 2018, 18, 140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Wongpakaran, T.; Wongpakaran, N.; Sirithepthawee, U.; Pratoomsri, W.; Burapakajornpong, N.; Rangseekajee, P.; Bookkamana, P.; Temboonkiat, A. Interpersonal problems among psychiatric outpatients and non-clinical samples. Singap. Med. J. 2012, 53, 481–487. [Google Scholar]
  5. Liu, C.H.; Stevens, C.; Wong, S.H.M.; Yasui, M.; Chen, J.A. The prevalence and predictors of mental health diagnoses and suicide among U.S. college students: Implications for addressing disparities in service use. Depress Anxiety 2019, 36, 8–17. [Google Scholar] [CrossRef] [PubMed]
  6. Desalegn, G.T.; Wondie, M.; Dereje, S.; Addisu, A. Suicide ideation, attempt, and determinants among medical students Northwest Ethiopia: An institution-based cross-sectional study. Ann. Gen. Psychiatry 2020, 19, 44. [Google Scholar] [CrossRef]
  7. Ibrahim, A.K.; Kelly, S.J.; Adams, C.E.; Glazebrook, C. A systematic review of studies of depression prevalence in university students. J. Psychiatr. Res. 2013, 47, 391–400. [Google Scholar] [CrossRef]
  8. Berman, A.L. Risk factors observed in the last 30 days of life among student suicides: Distinguishing characteristics of college and university student suicides. J. Am. Coll Health 2020, 1–5. [Google Scholar] [CrossRef]
  9. Nock, M.K.; Borges, G.; Bromet, E.J.; Alonso, J.; Angermeyer, M.; Beautrais, A.; Bruffaerts, R.; Chiu, W.T.; de Girolamo, G.; Gluzman, S.; et al. Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. Br. J. Psychiatry 2008, 192, 98–105. [Google Scholar] [CrossRef]
  10. Eskin, M.; AlBuhairan, F.; Rezaeian, M.; Abdel-Khalek, A.M.; Harlak, H.; El-Nayal, M.; Asad, N.; Khan, A.; Mechri, A.; Noor, I.M.; et al. Suicidal Thoughts, Attempts and Motives Among University Students in 12 Muslim-Majority Countries. Psychiatr Q 2019, 90, 229–248. [Google Scholar] [CrossRef]
  11. Asfaw, H.; Yigzaw, N.; Yohannis, Z.; Fekadu, G.; Alemayehu, Y. Prevalence and associated factors of suicidal ideation and attempt among undergraduate medical students of Haramaya University, Ethiopia. A cross sectional study. PLoS ONE 2020, 15, e0236398. [Google Scholar] [CrossRef]
  12. Peltzer, K.; Pengpid, S. Suicidal ideation and associated factors among school-going adolescents in Thailand. Int. J. Environ. Res. Public Health 2012, 9, 462–473. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Breet, E.; Matooane, M.; Tomlinson, M.; Bantjes, J. Systematic review and narrative synthesis of suicide prevention in high-schools and universities: A research agenda for evidence-based practice. BMC Public Health 2021, 21, 1116. [Google Scholar] [CrossRef] [PubMed]
  14. Heinen, I.; Bullinger, M. Kocalevent RD: Perceived stress in first year medical students—associations with personal resources and emotional distress. BMC Med. Educ. 2017, 17, 4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Desai, N.D.; Chavda, P.; Shah, S. Prevalence and predictors of suicide ideation among undergraduate medical students from a medical college of Western India. Med. J. Armed Forces India 2021, 77 (Suppl. 1), S107–S114. [Google Scholar] [CrossRef]
  16. Coentre, R. Góis C: Suicidal ideation in medical students: Recent insights. Adv. Med. Educ. Pract. 2018, 9, 873–880. [Google Scholar] [CrossRef] [Green Version]
  17. Blasco, M.J.; Vilagut, G.; Almenara, J.; Roca, M.; Piqueras, J.A.; Gabilondo, A.; Lagares, C.; Soto-Sanz, V.; Alayo, I.; Forero, C.G.; et al. Suicidal Thoughts and Behaviors: Prevalence and Association with Distal and Proximal Factors in Spanish University Students. Suicide Life Threat Behav. 2019, 49, 881–898. [Google Scholar] [CrossRef]
  18. Tan, S.T.; Sherina, M.S.; Rampal, L.; Normala, I. Prevalence and predictors of suicidality among medical students in a public university. Med. J. Malays. 2015, 70, 1–5. [Google Scholar]
  19. Xu, Y.; Wang, C.; Shi, M. Identifying Chinese adolescents with a high suicide attempt risk. Psychiatry Res. 2018, 269, 474–480. [Google Scholar] [CrossRef]
  20. Mitsui, N.; Asakura, S.; Takanobu, K.; Watanabe, S.; Toyoshima, K.; Kako, Y.; Ito, Y.M.; Kusumi, I. Prediction of major depressive episodes and suicide-related ideation over a 3-year interval among Japanese undergraduates. PLoS ONE 2018, 13, e0201047. [Google Scholar] [CrossRef]
  21. Patsali, M.E.; Mousa, D.V.; Papadopoulou, E.V.K.; Papadopoulou, K.K.K.; Kaparounaki, C.K.; Diakogiannis, I.; Fountoulakis, K.N. University students’ changes in mental health status and determinants of behavior during the COVID-19 lockdown in Greece. Psychiatry Res. 2020, 292, 113298. [Google Scholar] [CrossRef]
  22. Gelezelyte, O.; Kazlauskas, E.; Brailovskaia, J.; Margraf, J.; Truskauskaite-Kuneviciene, I. Suicidal ideation in university students in Lithuania amid the COVID-19 pandemic: A prospective study with pre-pandemic measures. Death Stud. 2021, 1–9. [Google Scholar] [CrossRef]
  23. Findlay, L. Depression and suicidal ideation among Canadians aged 15 to 24. Health Rep. 2017, 28, 3–11. [Google Scholar] [PubMed]
  24. Casale, M.; Boyes, M.; Pantelic, M.; Toska, E.; Cluver, L. Suicidal thoughts and behaviour among South African adolescents living with HIV: Can social support buffer the impact of stigma? J. Affect Disord 2019, 245, 82–90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Booniam, S.; Wongpakaran, T.; Lerttrakarnnon, P.; Jiraniramai, S.; Kuntawong, P.; Wongpakaran, N. Predictors of Passive and Active Suicidal Ideation and Suicide Attempt Among Older People: A Study in Tertiary Care Settings in Thailand. Neuropsychiatr. Dis. Treat 2020, 16, 3135–3144. [Google Scholar] [CrossRef] [PubMed]
  26. Oon-arom, A.; Wongpakaran, T.; Kuntawong, P.; Wongpakaran, N. Attachment anxiety, depression, and perceived social support: A moderated mediation model of suicide ideation among the elderly. Int. Psychogeriatr. 2021, 33, 169–178. [Google Scholar] [CrossRef]
  27. Kaplan, C.; Tarlow, N.; Stewart, J.G.; Aguirre, B.; Galen, G.; Auerbach, R.P. Borderline personality disorder in youth: The prospective impact of child abuse on non-suicidal self-injury and suicidality. Compr Psychiatry 2016, 71, 86–94. [Google Scholar] [CrossRef] [Green Version]
  28. Khosravi, M.; Kasaeiyan, R. The relationship between neuroticism and suicidal thoughts among medical students: Moderating role of attachment styles. J. Fam. Med. Prim. Care 2020, 9, 2680–2687. [Google Scholar] [CrossRef]
  29. Costa, A.C.B.; Mariusso, L.M.; Canassa, T.C.; Previdelli, I.T.S.; Porcu, M. Risk factors for suicidal behavior in a university population in Brazil: A retrospective study. Psychiatry Res. 2019, 278, 129–134. [Google Scholar] [CrossRef]
  30. Blasco, M.J.; Vilagut, G.; Alayo, I.; Almenara, J.; Cebrià, A.I.; Echeburúa, E.; Gabilondo, A.; Gili, M.; Lagares, C.; Piqueras, J.A.; et al. First-onset and persistence of suicidal ideation in university students: A one-year follow-up study. J. Affect. Disord. 2019, 256, 192–204. [Google Scholar] [CrossRef]
  31. Sar, V.; Akyuz, G.; Kugu, N.; Ozturk, E.; Ertem-Vehid, H. Axis I dissociative disorder comorbidity in borderline personality disorder and reports of childhood trauma. J. Clin. Psychiatry 2006, 67, 1583–1590. [Google Scholar] [CrossRef]
  32. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Diordrs, 5th ed.; American Psychiatric Association: Arlington, VA, USA, 2013. [Google Scholar]
  33. Peters, J.R.; Upton, B.T.; Baer, R.A. Brief report: Relationships between facets of impulsivity and borderline personality features. J. Pers. Disord. 2013, 27, 547–552. [Google Scholar] [CrossRef]
  34. Paris, J.; Zweig-Frank, H. A 27-year follow-up of patients with borderline personality disorder. Compr. Psychiatry 2001, 42, 482–487. [Google Scholar] [CrossRef]
  35. Meaney, R.; Hasking, P.; Reupert, A. Prevalence of Borderline Personality Disorder in University Samples: Systematic Review, Meta-Analysis and Meta-Regression. PLoS ONE 2016, 11, e0155439. [Google Scholar] [CrossRef]
  36. Chen, T.H.; Hsiao, R.C.; Liu, T.L.; Yen, C.F. Predicting effects of borderline personality symptoms and self-concept and identity disturbances on internet addiction, depression, and suicidality in college students: A prospective study. Kaohsiung J. Med. Sci. 2019, 35, 508–514. [Google Scholar] [CrossRef] [Green Version]
  37. Paris, J. Suicidality in Borderline Personality Disorder. Medicina 2019, 55, 223. [Google Scholar] [CrossRef] [Green Version]
  38. Romero-Acosta, K.; Verhelst, S.; Lowe, G.A.; Lipps, G.E.; Restrepo, J.; Fonseca, L. Association Between Suicidal Behaviour and Cannabis and Tranquilizer use, Depression, Aggression and Other Borderline Personality Traits Among Students in Sincelejo, Colombia. Rev. Colomb. Psiquiatr. 2021. [Google Scholar] [CrossRef]
  39. Andrewes, H.E.; Hulbert, C.; Cotton, S.M.; Betts, J.; Chanen, A.M. Relationships between the frequency and severity of non-suicidal self-injury and suicide attempts in youth with borderline personality disorder. Early Interv. Psychiatry 2019, 13, 194–201. [Google Scholar] [CrossRef]
  40. Wongpakaran, N.; Wongpakaran, T.; Kittipodjanasit, A.; Chompoosri, P.; Kuntawong, P.; Wedding, D. Predictive factors for suicidal attempts: A case-control study. Perspect Psychiatr. Care 2019, 55, 667–672. [Google Scholar] [CrossRef]
  41. Angstman, K.B.; Seshadri, A.; Marcelin, A.; Gonzalez, C.A.; Garrison, G.M.; Allen, J.S. Personality Disorders in Primary Care: Impact on Depression Outcomes within Collaborative Care. J. Prim. Care Community Health 2017, 8, 233–238. [Google Scholar] [CrossRef] [Green Version]
  42. Wongpakaran, N.; Wongpakaran, T.; Boonyanaruthee, V.; Pinyopornpanish, M.; Intaprasert, S. Comorbid personality disorders among patients with depression. Neuropsychiatr. Dis. Treat 2015, 11, 1091–1096. [Google Scholar] [CrossRef] [Green Version]
  43. Rizk, M.M.; Choo, T.H.; Galfalvy, H.; Biggs, E.; Brodsky, B.S.; Oquendo, M.A.; Mann, J.J.; Stanley, B. Variability in Suicidal Ideation is Associated with Affective Instability in Suicide Attempters with Borderline Personality Disorder. Psychiatry 2019, 82, 173–178. [Google Scholar] [CrossRef]
  44. Bennett, C.; Melvin, G.A.; Quek, J.; Saeedi, N.; Gordon, M.S.; Newman, L.K. Perceived Invalidation in Adolescent Borderline Personality Disorder: An Investigation of Parallel Reports of Caregiver Responses to Negative Emotions. Child Psychiatry Hum. Dev. 2019, 50, 209–221. [Google Scholar] [CrossRef]
  45. Zhou, S.J.; Wang, L.L.; Qi, M.; Yang, X.J.; Gao, L.; Zhang, S.Y.; Zhang, L.G.; Yang, R.; Chen, J.X. Depression, Anxiety, and Suicidal Ideation in Chinese University Students During the COVID-19 Pandemic. Front Psychol. 2021, 12, 669833. [Google Scholar] [CrossRef]
  46. Wang, Y.H.; Shi, Z.T.; Luo, Q.Y. Association of depressive symptoms and suicidal ideation among university students in China: A systematic review and meta-analysis. Medicine 2017, 96, e6476. [Google Scholar] [CrossRef]
  47. Rungsang, B.; Chaimongkol, N.; Deoisres, W.; Wongnam, P. Suicidal Ideation among Thai Adolescents: An Empirical Test of a Causal Model. Pac. Rim Int. J. Nurs. Res. 2017, 21, 97–107. [Google Scholar]
  48. Wongpakaran, N.; Wongpakaran, T.; Kuntawong, P. A short screening tool for borderline personality disorder (Short-Bord): Validated by Rasch analysis. Asian J. Psychiatr. 2019, 44, 195–199. [Google Scholar] [CrossRef]
  49. Zimet, G.D.; Powell, S.S.; Farley, G.K.; Werkman, S.; Berkoff, K.A. Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. J. Pers. Assess 1990, 55, 610–617. [Google Scholar]
  50. Wongpakaran, N.; Wongpakaran, T. A revised Thai Multi-Dimensional Scale of Perceived Social Support. Span J. Psychol. 2012, 15, 1503–1509. [Google Scholar] [CrossRef] [Green Version]
  51. Wongpakaran, N.; Wongpakaran, T. The Thai version of the PSS-10: An Investigation of its psychometric properties. BioPsychoSocial Med. 2010, 4, 6. [Google Scholar] [CrossRef] [Green Version]
  52. Kroenke, K.; Strine, T.W.; Spitzer, R.L.; Williams, J.B.; Berry, J.T.; Mokdad, A.H. The PHQ-8 as a measure of current depression in the general population. J. Affect. Disord. 2009, 114, 163–173. [Google Scholar] [CrossRef]
  53. Kroenke, K.; Spitzer, R.L.; Williams, J.B. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 2001, 16, 606–613. [Google Scholar] [CrossRef]
  54. Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments; Routledge: London, UK, 2021.
  55. Breet, E.; Kidd, M.; McGregor, N.W.; Stein, D.J.; Lochner, C. Suicide ideation and attempts in obsessive-compulsive disorder. Ann. Clin. Psychiatry 2019, 31, 192–199. [Google Scholar]
  56. Jiraniramai, S.; Wongpakaran, T.; Angkurawaranon, C.; Jiraporncharoen, W.; Wongpakaran, N. Construct Validity and Differential Item Functioning of the PHQ-9 Among Health Care Workers: Rasch Analysis Approach. Neuropsychiatr Dis. Treat. 2021, 17, 1035–1045. [Google Scholar]
  57. Mirkovic, B.; Delvenne, V.; Robin, M.; Pham-Scottez, A.; Corcos, M.; Speranza, M. Borderline personality disorder and adolescent suicide attempt: The mediating role of emotional dysregulation. BMC Psychiatry 2021, 21, 393. [Google Scholar] [CrossRef]
  58. Nisenbaum, R.; Links, P.S.; Eynan, R.; Heisel, M.J. Variability and predictors of negative mood intensity in patients with borderline personality disorder and recurrent suicidal behavior: Multilevel analyses applied to experience sampling methodology. J. Abnorm. Psychol. 2010, 119, 433–439. [Google Scholar] [CrossRef]
  59. Lew, B.; Huen, J.; Yu, P.; Yuan, L.; Wang, D.-F.; Ping, F.; Abu Talib, M.; Lester, D.; Jia, C.-X. Associations between depression, anxiety, stress, hopelessness, subjective well-being, coping styles and suicide in Chinese university students. PLoS ONE 2019, 14, e0217372. [Google Scholar] [CrossRef]
  60. Dyrbye, L.N.; Thomas, M.R.; Huntington, J.L.; Lawson, K.L.; Novotny, P.J.; Sloan, J.A.; Shanafelt, T.D. Personal life events and medical student burnout: A multicenter study. Acad. Med. 2006, 81, 374–384. [Google Scholar] [CrossRef]
  61. Lageborn, C.T.; Ljung, R.; Vaez, M.; Dahlin, M. Ongoing university studies and the risk of suicide: A register-based nationwide cohort study of 5 million young and middle-aged individuals in Sweden, 1993-2011. BMJ Open 2017, 7, e014264. [Google Scholar] [CrossRef] [Green Version]
  62. Dyrbye, L.N.; West, C.P.; Satele, D.; Boone, S.; Tan, L.; Sloan, J.; Shanafelt, T.D. Burnout among U.S. medical students, residents, and early career physicians relative to the general U.S. population. Acad. Med. 2014, 89, 443–451. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Quarshie, E.N.; Cheataa-Plange, H.V.; Annor, F.; Asare-Doku, W.; Lartey, J.K.S. Prevalence of suicidal behaviour among nursing and midwifery college students in Ghana. Nurs. Open 2019, 6, 897–906. [Google Scholar] [CrossRef]
  64. Hoying, J.; Melnyk, B.M.; Hutson, E.; Tan, A. Prevalence and Correlates of Depression, Anxiety, Stress, Healthy Beliefs, and Lifestyle Behaviors in First-Year Graduate Health Sciences Students. Worldviews Evid Based Nurs. 2020, 17, 49–59. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Wathelet, M.; Duhem, S.; Vaiva, G.; Baubet, T.; Habran, E.; Veerapa, E.; Debien, C.; Molenda, S.; Horn, M.; Grandgenèvre, P.; et al. Factors Associated with Mental Health Disorders Among University Students in France Confined During the COVID-19 Pandemic. JAMA Netw. Open 2020, 3, e2025591. [Google Scholar] [CrossRef] [PubMed]
  66. Pramukti, I.; Strong, C.; Sitthimongkol, Y.; Setiawan, A.; Pandin, M.G.R.; Yen, C.F.; Lin, C.Y.; Griffiths, M.D.; Ko, N.Y. Anxiety and Suicidal Thoughts During the COVID-19 Pandemic: Cross-Country Comparative Study Among Indonesian, Taiwanese, and Thai University Students. J. Med. Internet Res. 2020, 22, e24487. [Google Scholar] [CrossRef]
  67. Tyrer, P.; Mulder, R.; Crawford, M.; Newton-Howes, G.; Simonsen, E.; Ndetei, D.; Koldobsky, N.; Fossati, A.; Mbatia, J.; Barrett, B. Personality disorder: A new global perspective. World Psychiatry Off. J. World Psychiatr. Assoc. (WPA) 2010, 9, 56–60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Healthcare 09 01399 g001 550
Figure 1. Clustered bar graphs illustrating mean and confidence interval for each group using a bar chart with error bars. r-MSPSS = Revised Thai Multidimensional Scale of Perceived Social Support, PHQ-8 = Patient-Health Questionaire-8, SI-Bord = Short Instrument for Borderline Personality Disorder, T-PSS-10 Thai Version of Perceived Stress Scales.
Figure 1. Clustered bar graphs illustrating mean and confidence interval for each group using a bar chart with error bars. r-MSPSS = Revised Thai Multidimensional Scale of Perceived Social Support, PHQ-8 = Patient-Health Questionaire-8, SI-Bord = Short Instrument for Borderline Personality Disorder, T-PSS-10 Thai Version of Perceived Stress Scales.
Healthcare 09 01399 g001
Table
Table 1. Characteristics and demographic data of the overall participants (N = 336).
Table 1. Characteristics and demographic data of the overall participants (N = 336).
VariableMean (SD) or n (%)
Age 20.26 ± 1.4
SexFemale270 (80.4)
Number of years studying 2.58 ± 1.3
Academic major
Health Science254 (75.6)
Non-health Science82 (24.4)
Monthly allowance (THB)
<5000182 (54.2)
5001–10,000123 (36.6)
>10,00031 (9.2)
Satisfaction with monthly allowance
Yes258 (76.8)
No78 (23.2)
Psychological variable
Level of suicidal ideation
None288 (85.71)
Mild33 (9.82)
Moderate and severe15 (4.46)
SI-Bord3.80 ± 2.9
r-MSPSS53.09 ± 12.8
TPSS14.74 ± 6.5
PHQ-86.27 ± 4.9
r-MSPSS = Revised Thai Multidimensional Scale of Perceived Social Support, PHQ-8 = Patient-Health Questionaire-8, SI-Bord = Short Instrument for Borderline Personality Disorder, T-PSS-10 Thai Version of Perceived Stress Scales.
Table
Table 2. Characteristics and demographic data of the participants according to the level of suicidal ideation.
Table 2. Characteristics and demographic data of the participants according to the level of suicidal ideation.
Level of Suicidal IdeationTest Difference
VariableNoneMildModerate/Severe
N = 288N = 33N = 15
Age 20.20 ± 1.320.42 ± 1.721.07 ± 1.4F (2, 333) = 3.06, p = 0.048
SexFemale236 (87.4)25 (9.3)9 (3.3)χ2 = 4.84, df2, p = 0.089
Number of years studying2.51 ± 1.22.85 ± 1.43.4 ± 1.2F (2, 333) = 4.45, p = 0.012
Academic major
Health Science229 (90.2)19 (7.5)6 (2.4)χ2 = 18.51, df2, p < 0.001
Non-health Science59 (72.0)14 (17.1)9 (11.0)
Monthly Allowance (THB)
<5000163 (89.6)13 (7.1)6 (3.3)χ2 = 6.65, df4, p = 0.155
5001–10,000102 (82.9)14 (11.4)7 (5.7)
>10,00023 (74.2)6 (19.4)2 (6.5)
Satisfaction with monthly allowance
Yes227 (88.0)21 (8.1)10 (3.9)χ2 = 4.73, df2, p = 0.094
No61 (78.2)12 (15.4)5 (6.4)
Psychological variable
SI-Bord3.19 ± 2.56.52 ± 2.89.40 ± 2.5F (2, 333) = 64.06, p < 0.001
r-MSPSS55.35 ± 10.943.06 ± 13.233.20 ± 18.2F (2, 333) = 40.09, p < 0.001
TPSS13.42 ± 5.821.24 ± 5.325.27 ± 5.6F (2, 333) = 54.51, p < 0.001
PHQ-85.01 ± 4.110.33 ± 4.016.6 ± 4.2F (2, 333) = 74.46, p < 0.001
r-MSPSS = Revised Thai Multidimensional Scale of Perceived Social Support, PHQ-8 = Patient-Health Questionaire-8, SI-Bord = Short Instrument for Borderline Personality Disorder, T-PSS-10 Thai Version of Perceived Stress Scales.
Table
Table 3. Results of univariable ordinal regression analysis.
Table 3. Results of univariable ordinal regression analysis.
EstimateS.E.Walddfp-Value95% Confidence Interval
Lower BoundUpper Bound
Age0.2240.1114.04110.0440.0060.442
Year0.3190.1207.03510.0080.0830.555
Health Science1.2990.32116.33710.0000.6691.929
PHQ-80.3320.04069.01810.0000.2540.410
TPSS0.2760.03560.64710.0000.2070.346
SI-Bord0.4820.05965.73310.0000.3650.598
r-MSPSS−0.0860.01249.69810.000−0.110−0.062
S.E. = Standard Error, r-MSPSS = Revised Thai Multidimensional Scale of Perceived Social Support, PHQ-8 = Patient-Health Questionaire-8, SI-Bord = Short Instrument for Borderline Personality Disorder, T-PSS-10 Thai Version of Perceived Stress Scales.
Table
Table 4. Results of multivariable ordinal regression analysis.
Table 4. Results of multivariable ordinal regression analysis.
EstimateS.E.Walddfp-Value95% Confidence Interval
Lower BoundUpper BoundOdds Ratio (95% CI)
Age−0.0740.2510.08710.768−0.5670.4190.93 (0.59–1.46)
Year0.1300.2790.21810.640−0.4170.6771.14 (0.67–1.93)
Health Science0.7000.3963.11510.078−0.0771.4762.01 (0.93–4.36)
PHQ-80.1490.0537.80010.0050.0440.2531.16 (1.05–1.22)
TPSS0.1040.0455.29710.0210.0150.1931.11 (1.01–1.22)
SI-Bord0.1700.0804.47610.0340.0130.3281.19 (1.01–1.40)
r-MSPSS−0.0330.0154.57510.032−0.062−0.0030.97 (0.94–1.00)
S.E. = Standard Error, C I = Confidence Interval, r-MSPSS = Revised Thai Multidimensional Scale of Perceived Social Support, PHQ-8 = Patient-Health Questionaire-8, SI-Bord = Short Instrument for Borderline Personality Disorder, T-PSS-10 Thai Version of Perceived Stress Scales.
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Wongpakaran, N.; Oon-Arom, A.; Karawekpanyawong, N.; Lohanan, T.; Leesawat, T.; Wongpakaran, T. Borderline Personality Symptoms: What Not to Be Overlooked When Approaching Suicidal Ideation among University Students. Healthcare 2021, 9, 1399. https://doi.org/10.3390/healthcare9101399

AMA Style

Wongpakaran N, Oon-Arom A, Karawekpanyawong N, Lohanan T, Leesawat T, Wongpakaran T. Borderline Personality Symptoms: What Not to Be Overlooked When Approaching Suicidal Ideation among University Students. Healthcare. 2021; 9(10):1399. https://doi.org/10.3390/healthcare9101399

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Wongpakaran, Nahathai, Awirut Oon-Arom, Nuntaporn Karawekpanyawong, Trustsavin Lohanan, Thanakorn Leesawat, and Tinakon Wongpakaran. 2021. "Borderline Personality Symptoms: What Not to Be Overlooked When Approaching Suicidal Ideation among University Students" Healthcare 9, no. 10: 1399. https://doi.org/10.3390/healthcare9101399

APA Style

Wongpakaran, N., Oon-Arom, A., Karawekpanyawong, N., Lohanan, T., Leesawat, T., & Wongpakaran, T. (2021). Borderline Personality Symptoms: What Not to Be Overlooked When Approaching Suicidal Ideation among University Students. Healthcare, 9(10), 1399. https://doi.org/10.3390/healthcare9101399

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