Next Article in Journal
Impact of COVID-19 Pandemic Declaration on New Oncology Trial Commencements: An Interrupted Time Series with Segmented Regression Analysis
Next Article in Special Issue
The Impact of Psychological Distress on the Occupational Well-Being of Sexual and Gender Minorities
Previous Article in Journal
Antioxidant and Anti-Inflammatory Effects of Coenzyme Q10 Supplementation on Infectious Diseases
Previous Article in Special Issue
Individual Determinants of Rest-Break Behavior in Occupational Settings
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Healthcare
Volume 10
Issue 3
10.3390/healthcare10030488
healthcare-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prevalence and Associated Factors of Depression in Medical Students in a Northern Thailand University: A Cross-Sectional Study

by
Suwatthanachai Phomprasith
1,
Nuntaporn Karawekpanyawong
1,
Kanokporn Pinyopornpanish
2,
Wichuda Jiraporncharoen
2,
Benchalak Maneeton
1,
Phichayut Phinyo
2,3 and
Suppachai Lawanaskol
4,*
1
Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
2
Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
3
Center for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
4
Chaiprakarn Hospital, Chiang Mai 50320, Thailand
*
Author to whom correspondence should be addressed.
Healthcare 2022, 10(3), 488; https://doi.org/10.3390/healthcare10030488
Submission received: 14 January 2022 / Revised: 17 February 2022 / Accepted: 3 March 2022 / Published: 7 March 2022
(This article belongs to the Special Issue Psycho-Social Factors of Workplace Health and Safety)
Review Reports Versions Notes

Abstract

:
This study was conducted to investigate the prevalence and associated factors of depression in medical students. This cross-sectional study investigated the prevalence and associated factors of depression in medical students from May 2018 to April 2019. Depression was diagnosed using the nine-item Patient Health Questionnaire. We evaluated the following potential predictors: demographic data, stressors, psychiatric comorbidities, emotional intelligence (EI), and perceived social support. The association between potential factors and depression was analyzed using multiple logistic regression analysis. The prevalence of depression was 149 of 706 students with 12.5% suicidality. Second- and fourth-year medical students were high-risk groups. Risk factors identified were insufficient income, physical illness, and previous psychiatric illness. Depression in medical students likely coincides with anxiety, internet addiction, sleep problems, and loneliness. Highly associated stressors were personal relationships, physical health, mental health, difficulties in social relationships, satisfaction with grades, and boredom with medical education. Protective EI factors included emotional self-control, problem-solving abilities, inner peace, and life satisfaction. Up to 21.1% of medical students had depression. In this study, among multiple known risk factors of depression, we found that EI is the novel protective factor against depression among medical students. EI training might be protective intervention for medical students in the future.

1. Introduction

Depression can be characterized by persistent and recurrent sadness or lack of pleasure that eventually impairs a person’s functional ability to work, learn, or cope with daily life. The World Health Organization reports that the prevalence of depression worldwide is currently 4.4%. However, the prevalence of depression in medical students is reported to be 3–10 times higher (11.5–48.2%) [1,2,3]. Among Thai medical students, the prevalence of depression is 27% [4,5,6,7,8]. Depression is the most concerning mental health problem in medical students because it impacts students’ daily life, academic performance, and patient care [9,10]. Therefore, a comprehensive understanding of its impact and association is necessary to design preventive interventions. A study from Hawaii proposed the preventive intervention for depression, which consisted of faculty education, medical student counseling, and medical student education [11]. In Thailand, only screening and counseling is conducted to manage depression among medical students. Furthermore, no program for preventing depression has been established in Thailand. Exploring factors associated with depression among Thai medical students, especially protective factors, is beneficial for developing such a program.
Previous research has found an association between depression and relationship problems, lack of support systems, poor sleep quality, lack of motivation to study medicine, and pessimism [4,8]. Moreover, a Thai study in Vajira Hospital found an association between psychological abuse and depression [8].
Furthermore, many studies have shown that internet and game addiction have direct and indirect influences on anxiety, stress, and depression [12,13]. In addition, other psychiatric problems (anxiety, loneliness, and burnout) have been positively correlated with depression in many recent studies [14,15,16,17,18]. Conversely, emotional intelligence (EI) and perceived social support (PSS) are inversely correlated with depression and anxiety [19,20,21,22,23]. To our knowledge, these associated factors may also have correlations. Thus, adjusting for all factors that influence depression is necessary. Although many studies indicate associations between the said factors and depression, no study has suggested methods of adjusting all the potential factors of depression due to insufficient study size.
We hypothesized that a younger age (implying lower maturity) indicates worse mental health. Thus, an evaluation of depression throughout the years of study among Thai medical students is required. However, data on depression across the six years of medical school in Thailand are limited, especially in northern Thailand. Only one study was conducted across all year levels [8], and almost all studies in Thailand have been conducted among medical students in various levels of education [4,5,6,7]. Furthermore, studies of factors associated with depression in Thailand reported only problems but not protective factors.
For this reason, our primary objective was to investigate the prevalence of depression among northern Thai medical students. The secondary objective was to explore both potential risk and protective factors of depression across all year levels in medical school.

2. Materials and Methods

2.1. Study Design

This cross-sectional study was conducted at the Faculty of Medicine of Chiang Mai University in northern Thailand. The Research Ethics Committee of the Faculty of Medicine approved the study protocol (Reference Number 100/7003).

2.2. Research Setting

The Thai medical curriculum involves a six-year course and requires high school graduation for admission. The first-year curriculum consists of basic science lectures. Second- and third-year curricula involve a deeper study of medicine with lectures and problem-based learning of organ systems-based blocks. The last three years of medical school are the clinical training period.

2.3. Participants

We evaluated 1444 medical students of all year levels from May 2018 to April 2019. We contemporarily collect the data within a year across all year levels in medical school. Participants were asked to provide written consent and privately complete the anonymous self-reported questionnaires without receiving any compensation. Participants could withdraw at any time without giving a reason. Participants were limited to one response per person. The inclusion criteria of this study were current medical students, willing to participate, and having any device to conduct our online questionnaires. This study included both diagnosed and undiagnosed participants, providing a practical assessment of possible risk factors in a diverse population. We excluded students who did not provide consent or submitted incomplete questionnaires from further analysis.

2.4. Assessment of Potential Determinants for Depression

2.4.1. General Demographic Data

The questionnaires consisted of items on sociodemographic and health-related characteristics, including sex, age, year of study [8], hometown [4,24,25], relationship status [26], financial status [27], accommodations [28], underlying diseases (including mental health difficulties and substance usage), and family psychiatric history. We also collected information on perceived causes of stress, such as education, finance, relationships, health, and others. The students were further interviewed about their social adjustment ability, academic satisfaction, and reasons for studying medicine.

2.4.2. Psychiatric Problems

  • Anxiety: Generalized Anxiety Disorder-7 is a seven-item test. Anxiety was defined in this study as having a score of 10 or greater, with 89% sensitivity and 82% specificity [29].
  • Burnout: The Thai version of the Maslach Burnout Inventory–Student Survey is a 15-item questionnaire. Burnout status was determined as having high emotional exhaustion (>14 points), high depersonalization (>6 points), and low professional efficacy (<12 points) (Cronbach’s alpha of 0.79) [30].
  • Game addiction: The student version of the Game Addiction Screening Test has 16 items that assess problematic gaming behaviors in the past 3 months [31]. The cutoff for problematic game-playing was 24 points for male participants and 16 points for female participants (Cronbach’s alpha of 0.94).
  • Internet addiction: The Thai version of the 20-item Internet Addiction Test assesses internet compulsivity and addiction. The cutoff for internet addiction is >30 points (Cronbach’s alpha 0.89) [32,33].
  • Poor sleep quality: The Thai version of the Pittsburgh Sleep Quality Index (PSQI) is composed of seven components and evaluates sleep quality in the past month. Each component point ranges from 0 (no difficulty) to 3 (severe difficulty). A total PSQI > 5 was defined as poor sleep quality (Cronbach’s alpha 0.84) [34].
  • Loneliness: The six-item revised University of California–Los Angeles (UCLA) Loneliness Scale evaluates feelings of loneliness during the past week. The higher the score, the higher the feelings of loneliness (Cronbach’s alpha 0.83) [35].

2.4.3. Possible Protective Factors

  • Emotional intelligence (EI): The EI test for the Thai population aged 12–60 years was developed by the Mental Health Department of the Thai Ministry of Health. This 52-item self-report questionnaire consists of nine subscales. The interpretation for normal EI based on age for each subscale is presented in Appendix A Table A1 (overall Cronbach’s alpha 0.85) [36,37].
  • The revised Thai version of the Multi-Dimensional Scales of PSS (r-MSPSS) is a 12-item self-report measuring an individual’s experience of being supported by family, friends, and special persons. The higher the score, the higher the PSS level (Cronbach’s alpha of 0.91) [38].

2.4.4. Depression

The depression group was determined using the nine-item Patient Health Questionnaire (PHQ-9, Thai version), which evaluated the frequency of depressive symptoms occurring in the past 2 weeks. The recommended cutoff score is ≥9 for diagnosing major depression with a sensitivity of 0.84 and a specificity of 0.77 (compared with the Thai version of the Mini International Neuropsychiatric Interview) [39]. The severity of depression is further classified into three groups: mild (9–14 points), moderate (15–19 points), and severe (≥20 points) [40].

2.5. Study Size Estimation

In the previous study, the prevalence of depression among Thai medical students was 30.54% [8,12]. We confirmed the adequacy of the statistical power of the study size using W.G. Cochran’s equation [41], obtaining an alpha of 0.05 and an error of 0.05%. Thus, 326 participants were required to obtain adequate statistical power.

2.6. Statistical Analysis

Categorical data were expressed as frequency and percentage. Normally distributed continuous data were expressed as mean ± standard deviation (SD). Non-normally distributed continuous data were expressed as median and interquartile range. Univariable logistic regression was used in the effect estimation of each predictor.
Regarding the exploratory model strategy, we included all known risk factors of depression in a multivariable analysis without pre-selection from the preceding univariable analysis. According to enough events per variable (EPVs), at least 10 EPVs were required for multivariable binary logistic regression [42]. However, the prevalence of depression was less than 10 times the number of the related factor; thus, including all factors into a single multivariable model is unacceptable. Therefore, we adjusted this effect using the confounder summary score concept [43].
First, we divided all potential risk factors into five domains (demographic data, stressors, psychiatric problems, EI domain, and social support). Each predictor domain was analyzed using multivariable binary logistic regression to estimate the probability of depression. Five models were generated, one from each domain. Each domain was influenced by the others. The predicted values from each model containing known potential factors from the first step were included in the final model as regressors. The investigators performed statistical analysis using the STATA program version 16 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX, USA: StataCorp LLC.). The statistical significance was p < 0.05 (two-sided).

3. Results

The overall response rate was 48.9% (706 of 1444), including 77.7% (195 of 251) of first-year medical students, 45.7% (217 of 475) of late second-year students, and 40.9% (294 of 718) of third- to sixth-year students (clinical training period). The percentages of female medical students and of those who repeated a grade were similar between participants and total population (female: 48.7% of 48.9%; repeated a grade: 3.3% of 3.3%).
The mean age of the participants was 20.6 ± 2.0 years old, with no significant difference between the depression and non-depression groups. Among participants with depression, no significant difference in depression was found among those who received a higher income per month. However, a significant difference in depression was reported among those who received insufficient income. The depression group had significantly more medical comorbidities, psychiatric disorders, and family histories of psychiatric disorder. Although most students in the two groups drank alcohol less than once a week and did not exceed a standard measurement drink, more students in the depressed group reported using alcohol as self-medication to reduce stress and improve sleep. Only 4.7% of participants smoked (≤10 cigarettes per day), with no significant difference in smoking between the depression and non-depression groups. No significant differences were found in other demographic data (Table 1).

Depression

The prevalence of depression in this study was 21.1% (149 of 706). The most affected group were second- and fourth-year students. However, no significant differences were found among the year levels (p = 0.316). The rate of suicidality was 12.5% (88 of 706), with 62.5% (55 of 88) of students with suicidal ideation having some degree of depression. Sixth-year medical students had the highest rate of suicidality (Table 2).
Surprisingly, less than 20% (24 of 149) of participants in the depression group had ever made use of any mental health care service. About 70% (16 of 24) of them had been diagnosed with major depressive disorder (MDD); unfortunately, only 50% (7 of 16) of students with MDD continued treatment until the end of the study period.
All psychiatric problems and possible protective factors of interest were highly significant in univariable analysis (Table 3). Factors associated with depression from the five separate models using the confounder summary score model are shown in Table 2. Three demographic factors, six stressors, four psychiatric problems, and four EI domains were associated with depression. Confounder summary score was developed as shown in Table 4.

4. Discussion

This study found that the prevalence of depression in northern Thailand medical students was 21.1%, of which 12.5% had suicidal ideation. Furthermore, nearly 60% of sixth-year students who were depressed had suicidal ideation. Psychological problems associated with depression were anxiety, sleep problems, internet addiction, and loneliness. A possible protective factor against depression was EI, especially with regards problem-solving, life satisfaction, peace, and emotional self-control. Among medical students with depression, the significant stressors were mental health, relationships with loved ones, physical health, dissatisfaction with their course, and difficulty in social relationships. However, their satisfaction with grades had a positive effect on reducing depression. Demographic data related to depression included a history of psychiatric disorders, medical comorbidities, and sufficient income.

4.1. Prevalence

Medical students are undisputedly at a high risk of depression [3]. Prevalence of depression among Thai medical students is lower than the global rate (16.0–39.1% vs. 11.5–48.2%) [6,9,10,11,12], as confirmed in the present study. A higher prevalence of depression among second- and fourth-year medical students highlights the importance of the transitional periods in the course. The transition from learning basic science in the first year to learning medical concepts in the second year was frequently described as the most stressful transition due to changes in the learning environment, teaching styles, increased study burden, and competition among medical students. Moreover, the transition from the preclinical years to the first clinical year was the second most stressful transition. Many students reported experiencing a sense of lacking enough time and feelings of uselessness and inadequacy [44].
Our study emphasized the undertreatment of depression, which has remained unsolved for decades [28]. Medical students, whose teachers are doctors, nevertheless reported obstacles to accessing mental health care, usually due to a lack of time, stigmatization from themselves or others, and the fear of not achieving good grades and sabotaging their future career. More proactive solutions are required, which includes an increased role of the medical professors, not just the psychiatrists. In our opinion, medical professors have essential roles in solving these problems in many ways, for example:
  • Providing time in the curriculum for students to take care of themselves;
  • Identifying students highly prone to depression and providing sources of help;
  • Taking a stand to reduce the stigmatization of depression; and
  • Using positive communication, such as humor [45].

4.2. Factors Associated with Depression in Medical Students

4.2.1. Significant Psychiatric Problems

The significant association between anxiety and depression among medical students is not surprising, but the screening and management plan is. Anxiety co-occurs with depression frequently because of similar etiologies, such as genetic vulnerability, neuroticism, and elevated corticotropin-releasing factors [46,47]. In addition, anxiety increases the risk for depression due to increased impairment and debilitating factors [48]. Anxiety and depression, when combined, are more severe, more disabling, more resistant to treatment, and presents a greater risk of suicide than either disorder alone [49].
Robust research has shown sleep problems are a risk factor and symptom of depression [50]. Sleep problems involve a similar pathophysiology to depression, such as increased inflammatory cytokine expression, alterations in the level of monoamines (serotonin and norepinephrine), and interruption of the circadian rhythm. A genetic overlap between insomnia and depression has also been reported [51]. Moreover, the co-occurrence of sleep disturbance and depression decreases treatment response and increases depression severity, duration, and relapse rates [52,53].
We found that internet addiction was highly associated with depression. Depressed students tended to use the internet for social support, achievement, and pleasure of control, which can alleviate depressed feelings. However, at the same time, excessive internet use is a risk factor for depression because online communication displaces actual social communication, resulting in an ineffective ability to cope with difficult real-life situations [54].
Consistent with previous research [55], this study found an association between a high loneliness score with depression among medical students. Therefore, we hypothesized that we could reduce depression by reducing loneliness. To our knowledge, four primary intervention strategies for reducing loneliness have been identified: improving social skills, enhancing social support, increasing opportunities for social contact, and addressing maladaptive social cognition [56].

4.2.2. EI

We found that EI is a possible protective factor against depression, especially in the emotional self-control, problem-solving, peace, and life satisfaction domains.
Emotional self-control includes the ability to process negative thoughts and feelings. Inability to manage negative thoughts causes depression by overstimulating the hypothalamic pituitary adrenal axis and increasing cortisol production. Conversely, a sense of self-control raises self-esteem and decreases depression [57]. Problem-solving skills can buffer the negative correlation between stress and depression. Prior research has shown the effectiveness of problem-solving therapy for preventing and treating depression [58]. Peace, the Eastern concept of happiness, is a well-established protective factor against depression. In addition, showing gratitude can alleviate depressive symptoms [59]. Individual perception of life satisfaction depends not on how much they have but on how they feel. Ways to increase life satisfaction or decrease depression identified in previous studies include optimal self-esteem, good social interactions, and a peaceful environment [58].
Programs for improving EI improving may improve physician wellness and burnout problems [60]. However, no EI improvement programs have been included in Thailand’s conventional medical program. Thus, our study might contribute to a national depression prevention program.

4.2.3. Stressors

Although medical students with depression in this study are twice as likely to be concerned about mental health than individuals without depression, the rate of treatment for depression was relatively low. This finding implies that access to mental health services might be caused not only by low awareness, but also by insufficient knowledge on depression. In addition, having poor mental health impacts their functioning, reducing self-esteem and developing depression. Physical health also affects depression because it affects students’ ability to work or learn effectively [61].
Failure in or obstruction to psychosocial development (romantic relationship problems or difficulty in adjusting social relationships) can cause depression. The impact of age on an individual is discussed in Erik Erikson’s psychosocial development transition periods of “identity vs. role confusion” and “intimacy vs. isolation.” Intimacy not only means romantic relationships, but also friendships and other social relationships. Individuals need much more energy to adjust themselves and progress to the next development stage [62].
Academic boredom might be caused by feelings of anhedonia, leading to depression and, consequently, reduced learning performance and engagement [63]. Some studies found an association of boredom with inattention, anxiety, and depression [64]. Conversely, satisfaction with grades is, unsurprisingly, a possible protective factor against depression.

4.2.4. Demographic Data

As mentioned previously, having a psychiatric or medical illness increases the risk for depression. Medical students with depression showed a significant association between depression and perceived insufficiency of income, although they have a higher actual income per month. As also reported in a previous study, a student’s perception of their financial status, rather than their actual financial status, was significantly associated with depression [65].

4.2.5. Limitations

Our study has some limitations. First, our findings could not explain the causality because of the cross-sectional design. Second, the actual prevalence of depression might be higher than reported because of selection bias. Medical students with severe depression might have been unable to complete the questionnaire because of poor concentration or lack of energy, and thus, might have been excluded from the analysis. However, our results might still be representative of the target population, specifically in northern Thailand, given no significant differences in the percentage of sex and medical students repeating a grade between participants and all medical students. Nevertheless, our findings have limited generalizability to other populations.

5. Conclusions

One-fifth of the medical students in this study had depression. Medical students at high risk for depression were those with medical comorbidities, any history of psychiatric disorders, and perceived insufficient income. Medical students facing problems in the following aspects should be monitored for depression: romantic relationships, physical health, mental health, difficulties in social relationships, satisfaction with grades, and boredom with education. Consultants should consider depression in medical students with the following psychiatric comorbidities: anxiety, internet addiction, poor sleep quality, and loneliness. Possible protective factors against depression were EI factors, such as emotional self-control, problem-solving, inner peace, and life satisfaction. Early detection is not enough, and continuous care is crucial. Integrating EI improvement programs might help mitigate depression and should be investigated further.

Author Contributions

N.K., B.M. and S.P. conceptualized study. N.K., S.P., K.P. and W.J. designed the methods. N.K. and S.P. collected information. N.K. and S.L. coded and N.K., P.P. and S.L. analyzed the data. N.K., S.P. and S.L. interpreted the data and produced the first draft of manuscript. All authors refined and approved the final version for submission. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant from the Faculty of Medicine, Chiang Mai University, Thailand (grant number 100/2563), and Chiang Mai University, Thailand (grant number 15/2565).

Institutional Review Board Statement

The Research Ethics Committee, Faculty of Medicine, Chiang Mai University, has approved this study protocol (reference number 100/7003, 18 March 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. With electronically and privately inform, embed into the electronic questionnaire. Participants give the consent and the answer at the same time.

Data Availability Statement

All our anonymized dataset files are available from the online database (DOI: 10.6084/m9.figshare.16676725.)

Acknowledgments

The authors are appreciative to all medical students who participated in this study.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. The normal range of EI score in each subscale regarding age.
Table A1. The normal range of EI score in each subscale regarding age.
18–25 Years Old26–60 Years Old
Virtue subscaleEmotional self-control13–1814–18
Empathy16–2116–21
Responsibility17–2318–23
Competence subscaleSelf-motivation15–2116–22
Problem-solving14–2015–21
Interpersonal relationships15–2015–21
Happiness subscaleSelf-regard9–1410–14
Life satisfaction16–2216–22
Peace15–2116–22

References

  1. World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates; World Health Organization: Geneva, Switzerland, 2017.
  2. Kongsomboon, K. Psychological problems and overweight in medical students compared to students from Faculty of Humanities, Srinakharinwirot University, Thailand. J. Med. Assoc. Thail. 2011, 93, 106. [Google Scholar]
  3. Al-faris, E.A.; Irfan, F.; Van Der Vleuten, C.P.M.; Naeem, N.; Alsalem, A.; Alamiri, N.; Alraiyes, T.; Alfowzan, M.; Alabdulsalam, A.; Ababtain, A.; et al. The prevalence and correlates of depressive symptoms from an Arabian setting: A wake up call. Med. Teach. 2012, 34, S32–S36. [Google Scholar] [CrossRef] [PubMed]
  4. Limsricharoen, K.; Handee, N.; Chulakdabba, S. Prevalence and associated factors of depression in second to sixth years medical students, Faculty of Medicine in Thailand. J. Psychiatr. Assoc. Thail. 2014, 59, 29–40. [Google Scholar]
  5. Wongsiri, K. Assessment of depression among medical students between Medical Education Center and University Hoepital. Maharaj Nokorn Si Thammarat Med. J. 2017, 1, 27–33. [Google Scholar]
  6. Auchayasawat, S. Prevalence and Factors Associated with Depression among the Clinical Medical Students of a Faculty of Medicine in Northeast Thailand. Srinagarind Med. J. 2021, 36, 200–208. [Google Scholar]
  7. Phanpanich, P.; Manwong, M.; Vongsuphakphan, P.; Udomsuk, L. Depression in the First to Third Year Medical Students of College of Medicine and Pubic Health, Ubonratchathani University. Srinagarind Med. J. 2021, 36, 287–294. [Google Scholar]
  8. Kolkijkovin, V.; Phutathum, S.; Natetaweewat, N.; Joodthong, P.; Phaisanwongdee, P.; Nateetaweewat, P.; Jantarawattanawanich, A. Prevalence and associated factors of depression in medical students at Faculty of Medicine Vajira Hospital, Urban University. J. Med. Assoc. Thail. 2019, 102, 104–108. [Google Scholar]
  9. Mihăilescu, A.I.; Diaconescu, L.V.; Ciobanu, A.M.; Donisan, T.; Mihailescu, C. The impact of anxiety and depression on academic performance in undergraduate medical students. Eur. Psychiatry 2016, 33, s284. [Google Scholar] [CrossRef]
  10. Dyrbye, L.N.; Thomas, M.R.; Shanafelt, T.D. Medical student distress: Causes, consequences, and proposed solutions. In Mayo Clinic Proceedings; Elsevier: Amsterdam, The Netherlands, 2005; pp. 1613–1622. [Google Scholar]
  11. Thompson, D.; Goebert, D.; Takeshita, J. A program for reducing depressive symptoms and suicidal ideation in medical students. Acad. Med. 2010, 85, 1635–1639. [Google Scholar] [CrossRef]
  12. Bicholkar, A.U.; Dias, A.; Mascarenhas, V. Prevalence of Problematic Online Gaming among Undergraduate Medical Students and Its Relation to Well-Being, Self-Esteem and Depressive Mood in Goa, India. Int. J. Community Med. Public Health 2019, 6, 904–909. [Google Scholar] [CrossRef] [Green Version]
  13. Varma, P.; Cheasakul, U. The influence of game addiction and internet addiction among university students on depression stress and anxiety mediated by self-regulation and social support. J. Bus. Adm. Assoc. Priv. High. Educ. Inst. Thail. 2016, 5, 45–57. [Google Scholar]
  14. Wang, J.-L.; Sheng, J.-R.; Wang, H.-Z. The association between mobile game addiction and depression, social anxiety, and loneliness. Front. Public Health 2019, 7, 247. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Swami, V.; Chamorro-Premuzic, T.; Sinniah, D.; Maniam, T.; Kannan, K.; Stanistreet, D.; Furnham, A. General health mediates the relationship between loneliness, life satisfaction and depression. Soc. Psychiatry Psychiatr. Epidemiol. 2007, 42, 161–166. [Google Scholar] [CrossRef] [PubMed]
  16. Wolf, M.R.; Rosenstock, J.B. Inadequate sleep and exercise associated with burnout and depression among medical students. Acad. Psychiatry 2017, 41, 174–179. [Google Scholar] [CrossRef] [PubMed]
  17. Çelik, N.; Ceylan, B.; Ünsal, A.; Çağan, Ö. Depression in health college students: Relationship factors and sleep quality. Psychol. Health Med. 2019, 24, 625–630. [Google Scholar] [CrossRef]
  18. Brenneisen Mayer, F.; Souza Santos, I.; Silveira, P.S.P.; Itaqui Lopes, M.H.; de Souza, A.R.N.D.; Campos, E.P.; de Abreu, B.A.L.; Hoffman Ii, I.; Magalhães, C.R.; Lima, M.C.P. Factors associated to depression and anxiety in medical students: A multicenter study. BMC Med. Educ. 2016, 16, 282. [Google Scholar] [CrossRef]
  19. Ng, C.G.; Mohamed, S.; See, M.H.; Harun, F.; Dahlui, M.; Sulaiman, A.H.; Zainal, N.Z.; Taib, N.A. Anxiety, depression, perceived social support and quality of life in Malaysian breast cancer patients: A 1-year prospective study. Health Qual. Life Outcomes 2015, 13, 205. [Google Scholar] [CrossRef] [Green Version]
  20. Talwar, P.; Othman, M.K.; Ghan, K.A.; Wah, T.K.; Aman, S.; Yusoff, N.F.M. The role of social support in mediating stress and depression. Online J. Health Allied. Scs 2017, 16, 4. [Google Scholar]
  21. Amirifard, N.; Payandeh, M.; Aeinfar, M.; Sadeghi, M.; Sadeghi, E.; Ghafarpor, S. A survey on the relationship between emotional intelligence and level of depression and anxiety among women with breast cancer. Int. J. Hematol.-Oncol. Stem Cell Res. 2017, 11, 54. [Google Scholar]
  22. Syarif, Z. Association between Depression and Emotional Intelligence among Medical Students in Gadjah Mada University; Universitas Gadjah Mada: Yogyakarta, Indonesia, 2017. [Google Scholar]
  23. Karawekpanyawong, N.; Wongpakaran, T.; Wongpakaran, N.; Boonnag, C.; Siritikul, S.; Chalanunt, S.; Kuntawong, P. Impact of Perceived Social Support on the Relationship between ADHD and Depressive Symptoms among First Year Medical Students: A Structural Equation Model Approach. Children 2021, 8, 401. [Google Scholar] [CrossRef]
  24. Pieh, C.; Dale, R.; Jesser, A.; Probst, T.; Plener, P.L.; Humer, E. The Impact of Migration Status on Adolescents’ Mental Health during COVID-19. Healthcare 2022, 10, 176. [Google Scholar] [CrossRef] [PubMed]
  25. Foo, S.Q.; Tam, W.W.; Ho, C.S.; Tran, B.X.; Nguyen, L.H.; McIntyre, R.S.; Ho, R.C. Prevalence of depression among migrants: A systematic review and meta-analysis. Int. J. Environ. Res. Public Health 2018, 15, 1986. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Vujeva, H.M.; Furman, W. Depressive symptoms and romantic relationship qualities from adolescence through emerging adulthood: A longitudinal examination of influences. J. Clin. Child Adolesc. Psychol. 2011, 40, 123–135. [Google Scholar] [CrossRef] [PubMed]
  27. Mao, Y.; Zhang, N.; Liu, J.; Zhu, B.; He, R.; Wang, X. A systematic review of depression and anxiety in medical students in China. BMC Med. Educ. 2019, 19, 327. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Givens, J.L.; Tjia, J. Depressed medical students’ use of mental health services and barriers to use. Acad. Med. 2002, 77, 918–921. [Google Scholar] [CrossRef]
  29. Spitzer, R.L.; Kroenke, K.; Williams, J.B.; Löwe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch. Intern. Med. 2006, 166, 1092–1097. [Google Scholar] [CrossRef] [Green Version]
  30. Puranitee, P.; Saetang, S.; Sumrithe, S.; Busari, J.O.; van Mook, W.N.K.A.; Heeneman, S. Exploring burnout and depression of Thai medical students: The psychometric properties of the Maslach Burnout Inventory. Int. J. Med. Educ. 2019, 10, 223–229. [Google Scholar] [CrossRef] [Green Version]
  31. Pornnoppadol, C.; Sornpaisarn, B.; Khamklieng, K.; Pattana-amorn, S. The development of game addiction screening test (GAST). J. Psychiatr. Assoc. Thail. 2014, 59, 3–14. [Google Scholar]
  32. Young, K. Internet addiction: The emergence of a new clinical disorder. Cyber Psychol. Behav. 1998, 1, 237–244. [Google Scholar] [CrossRef] [Green Version]
  33. Neelapaijit, A.; Pinyopornpanish, M.; Simcharoen, S.; Kuntawong, P.; Wongpakaran, N.; Wongpakaran, T. Psychometric properties of a Thai version internet addiction test. BMC Res. Notes 2018, 11, 69. [Google Scholar] [CrossRef] [Green Version]
  34. Sitasuwan, T.; Bussaratid, S.; Ruttanaumpawan, P.; Chotinaiwattarakul, W. Reliability and validity of the Thai version of the Pittsburgh Sleep Quality Index. J. Med. Assoc. Thai 2014, 97, S57–S67. [Google Scholar] [PubMed]
  35. Wongpakaran, N.; Wongpakaran, T.; Pinyopornpanish, M.; Simcharoen, S.; Suradom, C.; Varnado, P.; Kuntawong, P. Development and validation of a 6-item Revised UCLA Loneliness Scale (RULS-6) using Rasch analysis. Br. J. Health Psychol. 2020, 25, 233–256. [Google Scholar] [CrossRef] [PubMed]
  36. Wongpiromsarn, Y.; Lortrakul, P.; Wanitrommanee, K.; Inseeyoung, V.; Sukmak, K.; Usaha, S. The Development of the emotional intelligence screening test for the Thai population aged 12 to 60 years. J. Psychaitr. Assoc. Thail. 2002, 47, 267–280. [Google Scholar]
  37. Siriwan, U.; Thitikalaya, N.; Soonthonsmai, V.; Ramabut, C.; Plomelersee, S.; Ha, M. A confirmatory factor analysis of the emotional quotient management model. Int. J. Arts Sci. 2015, 8, 303. [Google Scholar]
  38. 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]
  39. Lotrakul, M.; Sumrithe, S.; Saipanish, R. Reliability and validity of the Thai version of the PHQ-9. BMC Psychiatry 2008, 8, 46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Kaewpornsawan, T.; Chaiudomsom, C. The prevalence and associated factors of depression among residents in training at Faculty of Medicine, Siriraj Hospital. J. Psychiatr. Assoc. Thail. 2014, 59, 41–50. [Google Scholar]
  41. Cochran, W.G. Sampling Techniques; John Wiley & Sons: Hoboken, NJ, USA, 2007. [Google Scholar]
  42. Peduzzi, P.; Concato, J.; Kemper, E.; Holford, T.R.; Feinstein, A.R. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 1996, 49, 1373–1379. [Google Scholar] [CrossRef]
  43. Tadrous, M.; Gagne, J.J.; Stürmer, T.; Cadarette, S.M. Disease risk score as a confounder summary method: Systematic review and recommendations. Pharmacoepidemiol. Drug Saf. 2013, 22, 122–129. [Google Scholar] [CrossRef] [Green Version]
  44. Radcliffe, C.; Lester, H. Perceived stress during undergraduate medical training: A qualitative study. Med. Educ. 2003, 37, 32–38. [Google Scholar] [CrossRef] [PubMed]
  45. Narula, R.; Chaudhary, V.; Narula, K.; Narayan, R. Depression, anxiety and stress reduction in medical education: Humor as an intervention. Online J. Health Allied Sci. 2011, 10, 7. [Google Scholar]
  46. Middeldorp, C.; Cath, D.; Van Dyck, R.; Boomsma, D. The co-morbidity of anxiety and depression in the perspective of genetic epidemiology. A review of twin and family studies. Psychol. Med. 2005, 35, 611–624. [Google Scholar] [PubMed]
  47. Boyer, P. Do anxiety and depression have a common pathophysiological mechanism? Acta Psychiatr. Scand. 2000, 102, 24–29. [Google Scholar] [CrossRef]
  48. Wittchen, H.U.; Kessler, R.; Pfister, H.; Höfler, M.; Lieb, R. Why do people with anxiety disorders become depressed? A prospective-longitudinal community study. Acta Psychiatr. Scand. 2000, 102, 14–23. [Google Scholar] [CrossRef]
  49. Tiller, J.W. Depression and anxiety. Med. J. Aust. 2013, 199, S28–S31. [Google Scholar] [CrossRef]
  50. Rosenström, T.; Jokela, M.; Puttonen, S.; Hintsanen, M.; Pulkki-Råback, L.; Viikari, J.S.; Raitakari, O.T.; Keltikangas-Järvinen, L. Pairwise measures of causal direction in the epidemiology of sleep problems and depression. PLoS ONE 2012, 7, e50841. [Google Scholar] [CrossRef]
  51. Fang, H.; Tu, S.; Sheng, J.; Shao, A. Depression in sleep disturbance: A review on a bidirectional relationship, mechanisms and treatment. J. Cell. Mol. Med. 2019, 23, 2324–2332. [Google Scholar] [CrossRef] [Green Version]
  52. Manglick, M.; Rajaratnam, S.M.; Taffe, J.; Tonge, B.; Melvin, G. Persistent sleep disturbance is associated with treatment response in adolescents with depression. Aust. N. Z. J. Psychiatry 2013, 47, 556–563. [Google Scholar] [CrossRef]
  53. Franzen, P.L.; Buysse, D.J. Sleep disturbances and depression: Risk relationships for subsequent depression and therapeutic implications. Dialogues Clin. Neurosci. 2008, 10, 473. [Google Scholar]
  54. Chi, X.; Liu, X.; Guo, T.; Wu, M.; Chen, X. Internet addiction and depression in chinese adolescents: A moderated mediation model. Front. Psychiatry 2019, 10, 816. [Google Scholar] [CrossRef]
  55. Richardson, T.; Elliott, P.; Roberts, R. Relationship between loneliness and mental health in students. J. Public Ment. Health 2017, 16, 48–54. [Google Scholar] [CrossRef] [Green Version]
  56. Masi, C.M.; Chen, H.-Y.; Hawkley, L.C.; Cacioppo, J.T. A meta-analysis of interventions to reduce loneliness. Personal. Soc. Psychol. Rev. 2011, 15, 219–266. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Compare, A.; Zarbo, C.; Shonin, E.; Van Gordon, W.; Marconi, C. Emotional regulation and depression: A potential mediator between heart and mind. Cardiovasc. Psychiatry Neurol. 2014, 2014, 24374. [Google Scholar] [CrossRef] [PubMed]
  58. Liang, H.; Chen, C.; Li, F.; Wu, S.; Wang, L.; Zheng, X.; Zeng, B. Mediating effects of peace of mind and rumination on the relationship between gratitude and depression among Chinese university students. Curr. Psychol. 2020, 39, 1430–1437. [Google Scholar] [CrossRef]
  59. Cuijpers, P.; de Wit, L.; Kleiboer, A.; Karyotaki, E.; Ebert, D.D. Problem-solving therapy for adult depression: An updated meta-analysis. Eur. Psychiatry 2018, 48, 27–37. [Google Scholar] [CrossRef]
  60. Uchino, R.; Yanagawa, F.; Weigand, B.; Orlando, J.P.; Tachovsky, T.J.; Dave, K.A.; Stawicki, S.P. Focus on emotional intelligence in medical education: From problem awareness to system-based solutions. Int. J. Acad. Med. 2015, 1, 9. [Google Scholar]
  61. Ishii, T.; Tachikawa, H.; Shiratori, Y.; Hori, T.; Aiba, M.; Kuga, K.; Arai, T. What kinds of factors affect the academic outcomes of university students with mental disorders? A retrospective study based on medical records. Asian J. Psychiatry 2018, 32, 67–72. [Google Scholar]
  62. Weisskirch, R.S. Psychosocial intimacy, relationships with parents, and well-being among emerging adults. J. Child Fam. Stud. 2018, 27, 3497–3505. [Google Scholar] [CrossRef]
  63. Sharp, J.G.; Sharp, J.C.; Young, E. Academic boredom, engagement and the achievement of undergraduate students at university: A review and synthesis of relevant literature. Res. Pap. Educ. 2020, 35, 144–184. [Google Scholar] [CrossRef]
  64. LePera, N. Relationships between boredom proneness, mindfulness, anxiety, depression, and substance use. New Sch. Psychol. Bull. 2011, 8, 15–25. [Google Scholar]
  65. Al Saadi, T.; Addeen, S.Z.; Turk, T.; Abbas, F.; Alkhatib, M. Psychological distress among medical students in conflicts: A cross-sectional study from Syria. BMC Med. Educ. 2017, 17, 173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. Participants’ demographic data.
Table 1. Participants’ demographic data.
Characteristics, n (%)Total
(n = 706)		Depression
(n = 149)		Non-Depression
(n = 557)		p-Value
Age (Mean ± SD)20.6 ± 2.020.6 ± 2.020.5 ± 2.00.622
Female344 (48.7)81 (54.4)263 (47.2)0.140
Hometown 0.482
Bangkok58 (8.2)11 (7.4)47 (8.4)
Chiang Mai213 (30.2)38 (25.5)175 (31.4)
Northern218 (30.9)51 (34.2)167 (30.0)
Other217 (30.7)49 (32.8)168 (30.2)
Repeating a grade23 (3.3)5 (3.4)18 (3.2)1.000
Relationship status 0.159
In relationship192 (27.2)47 (31.5)145 (26.0)
Single506 (71.7)99 (66.4)407 (73.1)
Other8 (1.1)3 (2.0)5 (0.9)
Income per month (Median (IQR))8000 (6000, 10,000)9000 (6500, 11,000)8000 (6000, 10,000)0.057
Sufficient income655 (92.8)130 (87.3)525 (94.3)0.007
Medical comorbidity138 (19.5)42 (28.2)96 (17.2)0.004
Previous psychiatric disorder54 (7.6)23 (15.4)31 (5.6)<0.001
Alcohol use397 (56.2)88 (59.1)309 (55.5)0.458
Once a month303 (41.9)62 (41.6)241 (43.3)
Once a week94 (13.3)26 (17.5)68 (12.2)
Reason of alcohol drinking
Socialization357 (50.6)74 (49.7)283 (50.8)0.438
Stress57 (8.1)25 (16.8)32 (5.8)<0.001
Insomnia17 (2.4)9 (6.0)8 (1.4)0.003
Smoking33 (4.7)9 (6.0)24 (4.3)0.245
Reason of smoking
Socialization17 (2.4)6 (4.0)11 (2.0)0.223
Stress10 (1.4)2 (1.3)8 (1.4)1.000
Other6 (0.8)1 (0.7)5 (0.9)1.000
Other substances using7 (1.0)2 (1.3)5 (0.9)0.454
Psychiatric problem in relatives39 (5.5)13 (8.7)26 (4.7)0.047
Live with 0.201
Parent(s)524 (74.2)117 (78.5)407 (73.1)
Alone119 (16.9)24 (16.1)95 (17.1)
Friends/Relatives/Couple63 (8.9)8 (5.4)55 (9.9)
Living place 0.337
Apartment/Condominium/Rent room233 (33.0)51 (34.2)182 (32.7)
University dormitory360 (51.0)80 (53.7)280 (50.3)
House113 (16.0)18 (12.1)95 (17.1)
SD, standard deviation; IQR, interquartile range. Number in bold are for significant p-value at <0.05 (two-sided).
Table
Table 2. Severity of depression according to year of study.
Table 2. Severity of depression according to year of study.
Year of StudyDepression,
n, (% of Total)		Severity of Depression,
n, (% of Depression)		Suicidal Ideation in Depression Group
MildModerateSevere
1 (n = 195)42 (21.5)36 (85.7)6 (14.3)0 (0.0)17 (40.5)
2 (n = 105)27 (25.7)23 (85.2)3 (11.1)1 (3.7)12 (44.4)
3 (n = 112)16 (14.3)13 (81.3)3 (18.8)0 (0.0)3 (18.8)
4 (n = 120)29 (24.2)26 (89.7)2 (6.9)1 (3.4)7 (24.1)
5 (n = 95)21 (22.1)16 (76.2)4 (19.0)1 (4.8)8 (38.1)
6 (n = 79)14 (17.7)11 (78.6)2 (14.3)1 (7.1)8 (57.1)
Total (n = 706)149 (21.1)125 (83.9)20 (13.4)4 (2.7)55 (36.9)
Table
Table 3. Psychiatric problems and possible protective factors correlated with depression.
Table 3. Psychiatric problems and possible protective factors correlated with depression.
Factors,
n (%)/Median (IQR)		Total
(n = 706)		Depression
(n = 149)		Non-Depression
(n = 557)		OR (95% CI)p-Value
Stress due to
Academic difficulties592 (83.9)137 (91.9)455 (81.7)2.56 (1.37–4.80)0.003
Financial problems149 (21.1)49 (32.9)100 (18.0)2.24 (1.49–3.36)<0.001
Relationship with friends251 (35.6)79 (53.0)172 (30.9)2.53 (1.75–3.65)<0.001
Relationship with one’s beloved147 (20.8)53 (35.6)94 (16.9)2.72 (1.82–4.07)<0.001
Relationship with family169 (23.9)62 (41.6)107 (19.2)3.00 (2.03–4.42)<0.001
Relationship with instructor69 (9.8)30 (20.1)39 (7.0)3.35 (2.00–5.61)<0.001
Physical health181 (25.6)68 (45.6)113 (20.3)3.30 (2.25–4.84)<0.001
Mental health205 (29.0)82 (55.0)123 (22.1)4.32 (2.95–6.31)<0.001
Difficulty in social relationship3 (2,5)4 (3,6)3 (2,5)1.25 (1.15–1.36)<0.001
Satisfy with own grade7 (5,8)5 (3,7)7 (5,8)0.75 (0.69–0.82)<0.001
Boredom to medical learning5 (3,7)7 (5,8)5 (3,6)1.44 (1.32–1.58)<0.001
Thinking that wrong decision in studying medicine69 (9.8)33 (22.1)36 (6.5)4.12 (2.46–6.88)<0.001
Anxiety38 (5.4)32 (21.5)6 (1.1)25.12 (10.27–61.44)<0.001
Burnout10 (1.4)7 (4.7)3 (0.5)9.10 (2.33–35.65)0.002
Game addiction36 (5.1)14 (9.4)22 (3.9)2.52 (1.26–5.06)0.009
Internet addiction420 (59.5)112 (75.2)308 (55.3)2.45 (1.63–3.68)<0.001
Poor sleep quality185 (26.2)74 (49.7)111 (19.9)3.96 (2.70–5.81)<0.001
Loneliness11 (8,15)16 (12.5,18.5)10 (8,14)1.29 (1.23–1.35)<0.001
EI
Emotional self-control20 (18,21)18 (16,20)20 (19,21)0.71 (0.65–0.77)<0.001
Empathy20 (19,22)19 (17,21)21 (19,22)0.79 (0.73–0.85)<0.001
Responsibility21 (19,22)20 (18,22)21 (20,23)0.83 (0.77–0.90)<0.001
Self-motivation 18 (16,21)16 (14,18)19 (17,21)0.68 (0.63–0.73)<0.001
Problem-solving 17 (15,19)15 (14,16)17 (16,19)0.66 (0.60–0.72)<0.001
Interpersonal relationships 18 (16,20)16 (14,18)18 (16,20)0.81 (0.76–0.87)<0.001
Self-regard12 (10,13)10 (9,12)12 (11,14)0.64 (0.58–0.70)<0.001
Life satisfaction20 (17,22)17 (15,19)20 (18,22)0.73 (0.69–0.78)<0.001
Peace20 (17,22)17 (15,19)21 (18,23)0.70 (0.66–0.75)<0.001
PSS
Significant others23 (20,26)21 (17,24)24 (21,26)0.86 (0.82–0.89)<0.001
Family23 (19,25.3)20 (16,23.5)24 (20,26)0.86 (0.82–0.89)<0.001
Friend24 (21,26)21 (18,24)24 (22,27)0.86 (0.82–0.89)<0.001
Total70 (61,77)62 (52,71)72 (64,79)0.94 (0.93–0.96)<0.001
IQR, interquartile range; OR, odds ratio; CI, confidence interval; EE, emotional exhaustion; DP, depersonalization; PE, professional efficacy; EI, emotional intelligence; PSS, perceived social support. Number in bold are for significant p-value at <0.05 (two-sided).
Table
Table 4. Results of multivariable binary logistic regression.
Table 4. Results of multivariable binary logistic regression.
DomainsaORSE95% CIp-Value
Model 1: demographic data
Female1.430.290.97,2.120.074
Age0.910.050.82,1.010.079
Repeating the grade1.040.570.35,3.060.942
Relationship status
In relationship
Single0.800.170.52,1.230.306
Other1.931.630.37,10.120.435
Sufficient income0.460.150.24,0.880.018
Medical comorbid1.790.401.16,2.760.009
History of psychiatric disorders3.151.011.67,5.92<0.001
Alcohol drinking1.060.220.71,1.600.764
Smoking1.580.710.66,3.790.304
Other substances using1.641.480.28,9.560.581
Family psychiatric problem1.510.580.71,3.220.282
People who live with
Parent(s)
Alone0.870.230.51,1.480.606
Friends/Relatives/Couple0.500.200.23,1.100.086
Model 2: stressors
Financial problem1.280.320.78,2.080.327
Relationship with friends1.460.350.92,2.320.113
Relationship with one’s beloved1.800.451.10,2.940.019
Relationship with family1.490.380.90,2.450.121
Relationship with instructor1.040.340.55,1.990.900
1.730.411.09,2.750.020
Mental health2.290.541.45,3.62<0.001
Difficulty in social relationship1.130.061.02,1.250.020
Satisfy with own grade0.800.040.73,0.89<0.001
Boredom to medical learning1.300.071.17,1.45<0.001
Thinking that wrong decision in studying medicine1.500.490.79,2.850.215
Model 3: psychiatric comorbidity
Anxiety10.975.534.09,29.45<0.001
Burnout4.864.080.94,25.190.060
Game addiction1.590.700.67,3.760.289
Internet addiction1.900.461.18,3.060.009
Poor sleep quality2.480.571.58,3.89<0.001
Loneliness1.210.031.15,1.28<0.001
Model 4: EI *
Emotional self-control (high)0.460.100.30,0.71<0.001
Empathy (normal and high)0.970.290.54,1.740.922
Responsibility (normal and high)0.690.330.27,1.760.443
Self-motivation (normal and high)0.860.380.36,2.050.739
Problem-solving (normal and high)0.310.100.16,0.59<0.001
Interpersonal relationships (normal and high)0.570.220.27,1.210.142
Self-regard (normal and high)0.800.280.41,1.580.523
Life satisfaction (normal and high)0.460.140.25,0.850.013
Peace (normal and high)0.360.130.18,0.740.005
Model 5: PSS
Significant others0.960.040.89,1.050.404
Family0.920.040.84,1.000.042
Friend0.950.040.88,1.020.164
Confounder summary score **
Demographic 18.0019.202.23,145.570.007
Stressors 33.5819.2410.92,103.25<0.001
Psychiatric comorbidity 28.6317.288.77,93.43<0.001
Emotional intelligence 10.546.623.08,36.07<0.001
Perceived social support 2.192.100.33,14.350.415
* EI subscales were compared between groups with (1) lower and (2) normal and higher scores. The emotional self-control subscale was grouped into (1) normal and (2) high EI groups because no medical students had low emotional self-control. ** Multivariable logistic regression analysis for risk factors of depression adjusted for confounder summary score. IQR, interquartile range; aOR, adjusted odds ratio; SE, standard error; CI, confidence interval; EI, emotional intelligence; PSS, perceived social support. Number in bold are for significant p-value at <0.05 (two-sided).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Phomprasith, S.; Karawekpanyawong, N.; Pinyopornpanish, K.; Jiraporncharoen, W.; Maneeton, B.; Phinyo, P.; Lawanaskol, S. Prevalence and Associated Factors of Depression in Medical Students in a Northern Thailand University: A Cross-Sectional Study. Healthcare 2022, 10, 488. https://doi.org/10.3390/healthcare10030488

AMA Style

Phomprasith S, Karawekpanyawong N, Pinyopornpanish K, Jiraporncharoen W, Maneeton B, Phinyo P, Lawanaskol S. Prevalence and Associated Factors of Depression in Medical Students in a Northern Thailand University: A Cross-Sectional Study. Healthcare. 2022; 10(3):488. https://doi.org/10.3390/healthcare10030488

Chicago/Turabian Style

Phomprasith, Suwatthanachai, Nuntaporn Karawekpanyawong, Kanokporn Pinyopornpanish, Wichuda Jiraporncharoen, Benchalak Maneeton, Phichayut Phinyo, and Suppachai Lawanaskol. 2022. "Prevalence and Associated Factors of Depression in Medical Students in a Northern Thailand University: A Cross-Sectional Study" Healthcare 10, no. 3: 488. https://doi.org/10.3390/healthcare10030488

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop