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Investigating Mental Health Outcomes of Undergraduates and Graduate Students in Taiwan during the COVID-19 Pandemic

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Title: Investigating Mental Health Outcomes of Undergraduates and Graduate Students in Taiwan during the COVID-19 Pandemic
Language: English
Authors: Ching-Hui Lin (ORCID 0000-0002-4785-2871), Szu-Yin Lin (ORCID 0000-0001-5083-0932), Bo-Hsien Hu (ORCID 0000-0003-1337-3036), C. Owen Lo (ORCID 0000-0001-8294-9524)
Source: Journal of American College Health. 2024 72(9):3402-3409.
Availability: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 8
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Stress Variables, COVID-19, Pandemics, College Students, Online Courses, School Closing, Interpersonal Relationship, Financial Problems, Student Experience, Mental Health, Psychological Patterns, Anxiety, Depression (Psychology)
Geographic Terms: Taiwan
DOI: 10.1080/07448481.2022.2162824
ISSN: 0744-8481
1940-3208
Abstract: Objective: This study is an exploration of the major stressors associated with the COVID-19 for students in higher education in Taiwan. Participants: The sample comprised 838 higher education students studying at various Taiwanese universities. Methods: A cross-sectional online survey was administered at different postsecondary institutions during the semi-lockdown period of COVID-19, which mandated online instruction. Machine learning was employed to determine the variables that most highly predicted students' mental health using R. Results: The findings revealed that COVID-19-related experiences, including social interactions, financial conditions, and educational experiences, were significantly associated with mental health outcomes. Particularly, loneliness are significantly related to social interactions and educational experiences. Conclusions: Findings revealed that Covid-19 impacted Taiwanese students' financial conditions, educational experiences, and social interactions, which were significant predictors of their mental health outcomes such as anxiety, loneliness and depression. The current study contributes to the gap in knowledge about mental health issues among postsecondary students during the pandemic.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1451923
Database: ERIC
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  Value: <anid>AN0181109497;acl01dec.24;2024Nov28.03:47;v2.2.500</anid> <title id="AN0181109497-1">Investigating mental health outcomes of undergraduates and graduate students in Taiwan during the COVID-19 pandemic </title> <p>Objective: This study is an exploration of the major stressors associated with the COVID-19 for students in higher education in Taiwan. Participants: The sample comprised 838 higher education students studying at various Taiwanese universities. Methods: A cross-sectional online survey was administered at different postsecondary institutions during the semi-lockdown period of COVID-19, which mandated online instruction. Machine learning was employed to determine the variables that most highly predicted students' mental health using R. Results: The findings revealed that COVID-19-related experiences, including social interactions, financial conditions, and educational experiences, were significantly associated with mental health outcomes. Particularly, loneliness are significantly related to social interactions and educational experiences. Conclusions: Findings revealed that Covid-19 impacted Taiwanese students' financial conditions, educational experiences, and social interactions, which were significant predictors of their mental health outcomes such as anxiety, loneliness and depression. The current study contributes to the gap in knowledge about mental health issues among postsecondary students during the pandemic.</p> <p>Keywords: College students; Covid-19; mental health; postsecondary education</p> <hd id="AN0181109497-2">Introduction</hd> <p>Since early 2020, the outbreak of the COVID-19 pandemic has had various effects on daily lives worldwide and therefore been regarded as a Public Health Emergency of International Concern (PHEIC).[<reflink idref="bib1" id="ref1">1</reflink>],[<reflink idref="bib2" id="ref2">2</reflink>] While nearly all countries have undergone lockdowns and practiced such measures as wearing masks and social distancing in public places,[<reflink idref="bib3" id="ref3">3</reflink>] Taiwan was initially considered to be exemplary in controlling the spread of the virus, but after 18 months of nearly unblemished success, in May 2021, the country experienced a surge of confirmed cases, and following a long streak of case-free days, the weekly confirmed cases quickly grew to more than 3100.[<reflink idref="bib4" id="ref4">4</reflink>] In response to the severity of infections, the Taiwan Center for Disease Control (CDC) raised the pandemic alert to level-3 and strengthened national restrictions and measures, effective from May 15 to July 27.[<reflink idref="bib5" id="ref5">5</reflink>] People at high risk of COVID-19 infections, including those with a history of travel, occupational exposure, contact with infected individuals, and clustering (TOCC), were required to complete a 14-day home quarantine and an additional seven days of "self-monitored health management,"[<reflink idref="bib6" id="ref6">6</reflink>] by which individuals were required to record their body temperatures daily, monitor their health conditions, maintain social distancing, and avoid visiting crowded places.</p> <p>In various ways, the pandemic and quarantine measures particularly impacted education, leading to major changes to student life. Schools at all levels in Taiwan, both public and private, were required to shut down, and virtually overnight, online learning became the norm. This drastic change from a normal school life to lockdown had negative consequences for students in higher education, which, as reported in prior studies, included lower general health conditions, increased stress and anxiety, restricted opportunities for socializing, loneliness due to isolation, financial concerns, and interrupted learning trajectories.[[<reflink idref="bib7" id="ref7">7</reflink>], [<reflink idref="bib9" id="ref8">9</reflink>]] While conditions have improved since, given that resurgences of the pandemic may be ongoing for months or years to come, there is a critical need to explore how the recent COVID-19 alert impacted student mental health, especially in the COVID-19 hot spots in larger cities.</p> <hd id="AN0181109497-3">Mental health during the pandemic</hd> <p>Various mental health issues associated with the COVID-19 pandemic experienced by the general public have been reported,[<reflink idref="bib10" id="ref9">10</reflink>] including loneliness, anxiety, and depression, largely due to constraints on physical movement and requirements of social distancing, which have eliminated social activities, changed people's lifestyles and work practices, and curtailed employment opportunities.[<reflink idref="bib11" id="ref10">11</reflink>] Researchers have documented that outbreaks of COVID-19 have resulted in fear of infection, mental distraction, frustration, and financial loss.[<reflink idref="bib12" id="ref11">12</reflink>],[<reflink idref="bib13" id="ref12">13</reflink>] Prior researchers exploring the effects of the COVID-19 pandemic have examined particular institutions or specific groups, such as health care workers,[<reflink idref="bib14" id="ref13">14</reflink>] older adults,[<reflink idref="bib15" id="ref14">15</reflink>] and the general population.[<reflink idref="bib10" id="ref15">10</reflink>],[<reflink idref="bib12" id="ref16">12</reflink>],[<reflink idref="bib16" id="ref17">16</reflink>]However, there has been growing concern regarding the impact of the pandemic on the mental health of students, known to be a vulnerable population.[<reflink idref="bib10" id="ref18">10</reflink>],[[<reflink idref="bib16" id="ref19">16</reflink>], [<reflink idref="bib18" id="ref20">18</reflink>], [<reflink idref="bib20" id="ref21">20</reflink>]] For example, researchers found that students in severely impacted regions in the US experienced high rates of anxiety/depression and financial instability associated with the epidemic.[<reflink idref="bib9" id="ref22">9</reflink>],[<reflink idref="bib11" id="ref23">11</reflink>] Other studies have also shown that after the initial stage of the outbreak, there was a significant increase in symptoms of stress, anxiety, and depressions among college students.[<reflink idref="bib21" id="ref24">21</reflink>],[<reflink idref="bib22" id="ref25">22</reflink>]</p> <p>A number of studies have been focused on the health issues of undergraduates and graduate students during the COVID-19 pandemic, but the findings may not be generalizable across settings as the level and timing of the impact vary from one country to another. Given that Taiwan experienced its biggest outbreak relatively late in the ongoing trajectory of the pandemic, the elevated alert and quarantine measures imposed after a period of security may have had particular effects on students and educational institutions. Therefore, the purpose of this study was to investigate the impact of COVID-19 on the mental health of Taiwanese students who were enrolled in higher education institutions during the country's first severe outbreak, taking into account social and demographic factors that affected their experience during this period, including their cultural backgrounds, geographic locations, and social economic status, such as that of economically disadvantaged and first-generation university students. The present study provides a novel analytic model for understanding ways to respond to such global crises and provide vulnerable individuals with the assistance they need. The study was guided by the following questions: how are students' socio-demographic characteristics associated with students' mental health (ie, anxiety, loneliness, and depression) during the pandemic? To what extent, what factors have the impact on students' mental health during the COVID-19 pandemic?</p> <hd id="AN0181109497-4">Methods</hd> <p></p> <hd id="AN0181109497-5">Measures</hd> <p>A cross-sectional survey was designed during the surge of COVID-19 pandemic in Taiwan in late June, 2021. The survey comprised students' socio-demographic characteristics (gender, institutional types, parental education level, first generation status, living arrangement, full-time or part-time enrollment, financial status), as well as items assessing the impact of COVID-19 on their educational experiences, financial situation, and social interactions on a 5-point Likert's scale, ranging from "1-strongly disagree" to "5-strongly agree."</p> <p>The indicator for anxiety was composed of four items, which were adapted from the Generalized Anxiety Disorder scale (GAD-4),[<reflink idref="bib23" id="ref26">23</reflink>] including "I feel stressed about my livelihood"; "Mentally, I feel anxious"; "Every day, I feel something is going on"; and "I feel uncertainty about my life and future." The mean score of the four items was the indicator of anxiety.</p> <p>To measure the extent of depression or other stress-related emotions caused by the COVID-19 pandemic, two items were adopted from the Patience Health Questionnaire (PHQ-2),[<reflink idref="bib24" id="ref27">24</reflink>] including "The COVID-19 pandemic makes me feel more stress or other negative emotions" and "The COVID-19 pandemic impacts my lifestyle." The mean score of the two items was the indicator of depression. Both anxiety and depression were assessed on a 5-point Likert's scale ranging from "1-strongly inconsistent" to "5-strongly consistent."</p> <p>Three items developed by the University of California, Los Angeles (UCLA-3)[<reflink idref="bib3" id="ref28">3</reflink>] were included to assess frequency of experiencing loneliness on a 5-point scale, ranging from "1-never" to "5-always": "I feel I lack company." "I feel blocked from others"; and "I feel left out by others." The construct of loneliness was measured by averaging the scores of the three items.</p> <p>The survey also had an open-ended question to invite students to express their thoughts on any aspect of the pandemic, including anything that was not covered in the survey. The final questionnaire contained 38 items and took 5–10 minutes to complete.</p> <hd id="AN0181109497-6">Procedures</hd> <p>A convenience sampling procedure was used for data collection during July, 2021, when all schools and campuses in Taiwan were locked down in response to the COVID-19 surge. Across 126 higher educational institutions, undergraduate and graduate students were invited to complete the survey, which was administered via the Internet for both convenience and safety considerations. The study had been approved as meeting ethical standards as defined by the Institutional Review Board (IRB) at the Kaohsiung Medical University in Taiwan (IRB Number: KMUHIRB-E(II)-20210178). Participation was voluntary, and no incentives to complete the survey were offered. Participants signed a consent form prior to proceeding. A total of 838 valid responses were received.</p> <hd id="AN0181109497-7">Data analysis</hd> <p>The software R 4.0.1[<reflink idref="bib25" id="ref29">25</reflink>] was used for data analysis. For casting the LASSO model estimation and the process cross-validation, functions embedded in the package "glmnet"[<reflink idref="bib26" id="ref30">26</reflink>] were applied. In addition, a correlation matrix was constructed to examine the relationships among the variables in order to build Least Absolute Shrinkage and Selection Operator (LASSO) regression models, a regularization technique in machine learning, using the significant variables to ensure model convergence for more accurate prediction.</p> <hd id="AN0181109497-8">Descriptive statistics</hd> <p>As shown in Table 1, 62.5% of the 838 respondents were female, and approximately 95% were undergraduates and master's degree students. About 44.2% students were enrolled in northern universities, 37.2% in southern universities, and 18.1% in central universities of Taiwan. More than half were full-time students, and 35.6% were working part-time. In terms of accommodations, nearly 38% of the students were living with their families, 37.6% in off-campus housing, and 24.1% in on-campus housing. The assessment of funding allowed for multiple responses to accommodate those drawing on various sources. On average, students reported moderate impact of COVID-19 on their lives. Only 15.8% students reported experiences associated with home quarantine and self-management of health. The mean level of impact on social interaction was 2.88, on, financial condition was 2.14, and on educational experiences 2.43. With respect to mental health outcomes, loneliness had the lowest mean, 1.56, while the means for anxiety and depression were 2.44 and 2.72 respectively (Table 2).</p> <p>Table 1. Descriptive statistics.</p> <p> <ephtml> <table><thead><tr><td>Items</td><td>Total n = 838</td><td>Items</td><td>Total n = 838</td></tr><tr><td>n (%)</td><td>n (%)</td></tr></thead><tbody valign="top"><tr><td>Sex</td><td /><td>University location</td><td /></tr><tr><td> Female</td><td char=".">524 (62.5%)</td><td> Northern</td><td char=".">370 (44.2%)</td></tr><tr><td> Male</td><td char=".">314 (37.5%)</td><td> Central</td><td char=".">152 (18.1%)</td></tr><tr><td>Age</td><td /><td> Southern</td><td char=".">312 (37.2%)</td></tr><tr><td> 18–21</td><td char=".">424 (50.6%)</td><td> Eastern</td><td char=".">4 (0.5%)</td></tr><tr><td> 22–25</td><td char=".">276 (32.9%)</td><td>Work status</td><td /></tr><tr><td> 26–30</td><td char=".">58 (6.9%)</td><td> None</td><td char=".">454 (54.2%)</td></tr><tr><td> 31–35</td><td char=".">27 (3.2%)</td><td> Full-time</td><td char=".">86 (10.3%)</td></tr><tr><td> 36 and above</td><td char=".">53 (6.3%)</td><td> Part-time</td><td char=".">298 (35.6%)</td></tr><tr><td>Student identity</td><td /><td>Part-time work hours</td><td /></tr><tr><td> Foreign</td><td char=".">22 (2.6%)</td><td> None</td><td char=".">475 (56.7%)</td></tr><tr><td> Indigenous</td><td char=".">21 (2.5%)</td><td> Below 5 hours</td><td char=".">123 (14.7%)</td></tr><tr><td> Taiwanese</td><td char=".">765 (91.3%)</td><td char="."> 6–10 hours</td><td char=".">106 (12.6%)</td></tr><tr><td> New immigrants</td><td char=".">30 (3.6%)</td><td char="."> 11–15 hours</td><td char=".">40 (4.8%)</td></tr><tr><td>Institutional types</td><td /><td char="."> 16–20 hours</td><td char=".">37 (4.4%)</td></tr><tr><td> Public</td><td char=".">489 (58.4%)</td><td> 20 hours and above</td><td char=".">57 (6.8%)</td></tr><tr><td> Private</td><td char=".">349 (41.6%)</td><td> Masters' and above</td><td char=".">125 (14.9%)</td></tr><tr><td>Level of education</td><td /><td> Vocational/High school</td><td char=".">277 (33.1%)</td></tr><tr><td> Undergraduate</td><td char=".">589 (70.3%)</td><td> Junior high school</td><td char=".">69 (8.2%)</td></tr><tr><td> Master's</td><td char=".">186 (22.2%)</td><td> Elementary school</td><td char=".">37 (4.4%)</td></tr><tr><td> Master's (part-time)</td><td char=".">32 (3.8%)</td><td>First generation student</td><td /></tr><tr><td> Doctoral</td><td char=".">31 (3.7%)</td><td> No</td><td char=".">603 (72.0%)</td></tr><tr><td>Accommodation</td><td /><td> Yes</td><td char=".">235 (28.0%)</td></tr><tr><td> With family</td><td char=".">315 (37.6%)</td><td>Financial aids</td><td /></tr><tr><td> With relatives</td><td char=".">6 (0.7%)</td><td> No</td><td char=".">727 (86.8%)</td></tr><tr><td> On campus dormitory</td><td char=".">202 (24.1%)</td><td> Yes</td><td char=".">111 (13.2%)</td></tr><tr><td> Off-campus housing</td><td char=".">252 (30.1%)</td><td>Tuition funding sources (multiple choice)</td></tr><tr><td> Shared flat/apartment</td><td char=".">63 (7.5%)</td><td> From parents</td><td char=".">567 (67.7%)</td></tr><tr><td>COVID19-related experience</td><td> Self-funded</td><td char=".">226 (27.0%)</td></tr><tr><td> No</td><td char=".">706 (84.2%)</td><td> Scholarship</td><td char=".">70 (8.4%)</td></tr><tr><td> Yes</td><td char=".">132 (15.8%)</td><td> Student loan</td><td char=".">134 (16.0%)</td></tr></tbody></table> </ephtml> </p> <p>Table 2. Mean and standard deviations.</p> <p> <ephtml> <table><thead><tr><td /><td>Mean (SD)</td><td>Median [Min, Max]</td></tr></thead><tbody valign="top"><tr><td>Impacts of Covid-19</td><td /><td /></tr><tr><td> Social interaction</td><td char=".">2.88 (0.846)</td><td char=".">3.00 [0, 4.00]</td></tr><tr><td> Financial condition</td><td char=".">2.14 (1.13)</td><td char=".">2.00 [0, 4.00]</td></tr><tr><td> Educational experiences</td><td char=".">2.43 (0.691)</td><td char=".">2.40 [0, 4.00]</td></tr><tr><td>Health outcomes</td><td /><td /></tr><tr><td> Loneliness</td><td char=".">1.56 (0.936)</td><td char=".">1.67 [0, 4.00]</td></tr><tr><td> Anxiety</td><td char=".">2.44 (0.968)</td><td char=".">2.50 [0, 4.00]</td></tr><tr><td> Depression</td><td char=".">2.72 (0.862)</td><td char=".">3.00 [0, 4.00]</td></tr></tbody></table> </ephtml> </p> <hd id="AN0181109497-9">LASSO regression</hd> <p>In machine learning, regularization is a well-known supervised statistical learning method for the selection of the subset of variables to keep a model more generalized and parsimonious. By adding a penalty term to the best fit produced by the trained data, regularization avoids overfitting of the data. This technique is effective for minimizing the number of variables in order to maintain the accuracy level of the model. It reaches the goal of variable selection by modifying the linear regression equation to turn the traditional linear regression into an optimization problem. The optimization problem behind the method can be written as follows[<reflink idref="bib27" id="ref31">27</reflink>] (Equation 1).</p> <p>Graph</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><munderover><mo stretchy="false">∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>n</mi></mrow></munderover><mrow><msup><mrow><mo stretchy="true">(</mo><mrow><msub><mrow><mi>y</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>−</mo><msub><mrow><mi>β</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>−</mo><mrow><munderover><mo stretchy="false">∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>p</mi></mrow></munderover><mrow><msub><mrow><mi>β</mi></mrow><mrow><mi>j</mi></mrow></msub><msub><mrow><mi>x</mi></mrow><mrow><mtext mathvariant="italic">ij</mtext></mrow></msub></mrow></mrow></mrow><mo stretchy="true">)</mo></mrow><mrow><mn>2</mn></mrow></msup><mo>+</mo><mi>λ</mi><mrow><munderover><mo stretchy="false">∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>p</mi></mrow></munderover><mrow><mo stretchy="true">|</mo><mrow><msub><mrow><mi>β</mi></mrow><mrow><mi>j</mi></mrow></msub></mrow><mo stretchy="true">|</mo></mrow></mrow></mrow></mrow></math> </ephtml> (<reflink idref="bib1" id="ref32">1</reflink>)</p> <p>Cross-validation (CV) is helpful in searching for the proper tuning parameter in LASSO regression.[<reflink idref="bib28" id="ref33">28</reflink>] Within the five-fold cross-validation method (5-fold CV), 588 cases were used as the training data in the present study.</p> <p>Notably, using all the data to form a predictive model may make the model too specific to be generalizable. Therefore, the dataset should be divided into a training set and a testing set. The former would be used for selecting variables and forming a predictive model by the LASSO method, and the latter would be used for testing the predictive model's generalizability. Generally, for obtaining a more reliable predictive model, the sample size of training data should be larger than that of the testing data.</p> <p>In the current study, 70% of data (N = 588) served as training data, and the remaining 30% as testing data. The data were divided through the "sample.split" function embedded in the "caTools" package, which can ensure that the mean values of a dependent variable in the training and testing data are close. It can thus reduce the fluctuation of results according to the sampling and maintain reliability. Due to the LASSO method being a kind of statistical learning approach, it also entails the dilemma of bias and variance. In order to strike a balance between bias and variance in the predictive model constructed by the LASSO method, the cross-validation (CV) procedure is adopted to help search for the proper tuning parameter in the LASSO regression. In the CV process, the training dataset is be split into multiple folds, five in the current study. These five folds were further divided into four folds serve as training folds and one fold that served as a testing fold. And then, the LASSO regression with a broad range of tuning parameters was conducted iteratively five times. The mean square error (MSE) for each time was considered a part of the cross-validation error (CVE). After five iterations, the tuning parameter that yielded a minimum CVE, which was be adapted to the construction of the predictive model. After the predictors were selected by the LASSO method with training data, a linear model for evaluating the effect of the chosen predictors on the dependent variables were fitted according to the testing data.</p> <p>The pair-wise correlation plot (Figure 1) showed positive correlations among indicators for negative emotions including anxiety, loneliness, and depression. Below, the results for these three main mental health outcomes as dependent variables are presented separately. The indicators for measuring the impact of the COVID-19 pandemic on financial condition, social interaction, and educational experience were also positively correlated. Moreover, source of tuition fees, age, levels of education, and students' job typed were correlated with each other. Considering that negative emotions are usually highly correlated, when one negative emotion served as the dependent variable, the other two negative emotions were excluded from the predictor. This way was helpful for exploring which combination of students' characteristics or COVID-19 related experiences were predictive of which negative emotions, instead of which negative emotions were predictive of others.</p> <p>PHOTO (COLOR): Figure 1. The pair-wise correlation between the items within the questionnaire. Red color represents positive correlation, and blue colors refers to negative correlation.</p> <hd id="AN0181109497-10">Results</hd> <p></p> <hd id="AN0181109497-11">Anxiety</hd> <p>A LASSO model with five-fold CV was conducted to search for an appropriate tuning parameter, and the equation λ = 0.065, which yielded the lowest cross-validated MSE in the five-fold CV, was chosen. The coefficient for the predictors and corresponding range of λ value in the LASSO model is visualized in Figure 2(a), where λ = 0.065 is represented by a dotted line.</p> <p>PHOTO (COLOR): Figure 2. (a) Numbers of predictors with non-zero coefficients along with the strength of tuning parameters (λ) in the LASSO model for predicting anxiety. (b) Correlation between variables selected by LASSO with measured anxiety.</p> <p>Under this penalty strength, there were eight dummied predictors from the remaining eight items in our questionnaire with coefficients that had not been shrunk to zero in the LASSO model (see Figure 2(b)). A multiple linear regression analysis of the testing set was conducted to evaluate the model using the eight items selected by the LASSO method to predict the anxiety indicator. The coefficients of only two variables, the impact of the pandemic on financial condition and on educational experience, are significant in the model. The full model containing eight items and the compact model containing only the two items with significant coefficients were submitted to a chi-square model comparison. A non-significant result (</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi>χ</mi></mrow><mrow><mo stretchy="true">(</mo><mrow><mn>13</mn></mrow><mo stretchy="true">)</mo></mrow></msub><mo>=</mo><mn>7.43</mn></math> </ephtml> , <emph>p</emph> =.76) suggested that the compact model provided enough fitness. The</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math> </ephtml> of the compact model accounts for about 23% of the anxiety that could be attributed to the predictors. The results (Table 3) revealed that students whose financial condition or educational experiences were affected by the pandemic felt more anxious than those who were not affected in these areas.</p> <p>Table 3. Results of the three compact linear models for each dependent variable regressed on the predictors selected by the LASSO method.</p> <p> <ephtml> <table><thead><tr><td>DV Predictors</td><td>Anxiety</td><td>Loneliness</td><td>Depression</td></tr><tr><td>Coefficient</td><td>SE</td><td><italic>p</italic></td><td>Coefficient</td><td>SE</td><td><italic>p</italic></td><td>Coefficient</td><td>SE</td><td><italic>p</italic></td></tr></thead><tbody valign="top"><tr><td>Financial condition</td><td char=".">.16</td><td char=".">.05</td><td char=".">.001</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Educational experiences</td><td char=".">.45</td><td char=".">.09</td><td char="."><.001</td><td char=".">.33</td><td char=".">.09</td><td char="."><.001</td><td char=".">.39</td><td char=".">.08</td><td char="."><.001</td></tr><tr><td>Social interaction</td><td>–</td><td>–</td><td>–</td><td char=".">.25</td><td char=".">.07</td><td char=".">.001</td><td char=".">.17</td><td char=".">.06</td><td char=".">.006</td></tr><tr><td>Observations</td><td char=".">251</td><td char=".">251</td><td char=".">250</td></tr><tr><td>R<sup>2</sup></td><td char=".">.229</td><td char=".">.229</td><td char=".">.237</td></tr></tbody></table> </ephtml> </p> <hd id="AN0181109497-12">Loneliness</hd> <p>Variables for predicting the loneliness indicator in the final multiple linear regression model were again selected by the LASSO method with five-fold CV. The tuning parameter was set as 0.065, and five out of 21 items with coefficients that were not shrunk to zero in the LASSO model were selected to predict the loneliness indicator (see Figure 3(a,b)). The result of a comparison between the full model and the compact model showed that the compact model provided adequate fitness (</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi>χ</mi></mrow><mrow><mo stretchy="true">(</mo><mrow><mn>10</mn></mrow><mo stretchy="true">)</mo></mrow></msub><mo>=</mo><mn>8.33</mn></math> </ephtml> , <emph>p</emph> =.32). The</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mi mathvariant="normal" /></math> </ephtml> showed that about 23% of loneliness could be explained by the two predictors. As shown in Table 3, students whose social interactions or educational experiences were affected by the pandemic reported more loneliness than students without such impact.</p> <p>PHOTO (COLOR): Figure 3. (a) Numbers of predictors with non-zero coefficients along with the strength of tuning parameters (λ) in the LASSO model for predicting loneliness. (b) Correlation between variables selected by LASSO with measured depression.</p> <hd id="AN0181109497-13">Depression</hd> <p>Based on a tuning parameter equal to 0.065, four items were selected by the LASSO model with coefficients that had not been shrunk to zero. Figure 4(a) presents the correlation between variables selected by LASSO with measured loneliness. The relations of the tuning parameters and numbers of non-zero predictors corresponding with their coefficients are presented in Figure 4(b). Among the four variables, two yielded significant coefficients in the linear regression model of the depression indicator. A comparison showed that there was no significant difference in fitness between the full model with four variables and the compact model with two variables, (</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi>χ</mi></mrow><mrow><mo stretchy="true">(</mo><mrow><mn>5</mn></mrow><mo stretchy="true">)</mo></mrow></msub><mo>=</mo><mn>5.85</mn></math> </ephtml> , <emph>p</emph> =.09), suggesting the two variables in the compact model provide prediction equivalent to the full model. As shown in Table 3, students whose social interactions or educational experiences were impacted by the COVID-19 pandemic felt significantly more depressed than those whose were not.</p> <p>PHOTO (COLOR): Figure 4. (a) Numbers of predictors with non-zero coefficients along with the strength of tuning parameters (λ) in the LASSO model for predicting depression. (b) Correlation between variables selected by LASSO with measured depression.</p> <hd id="AN0181109497-14">Discussion</hd> <p>In the present study, the relationships between Covid-19 related experiences and their impact on the mental health outcomes of college students in Taiwan were investigated. Findings revealed that the impact of Covid-19 on students' financial conditions, educational experiences, and social interactions were significant predictors of their mental health conditions. On the other hand, students' demographic backgrounds were not significantly associated with health outcomes, perhaps because Taiwan's diversity is relatively modest comparing to other countries. Also, although Taiwan Ministry of Education reported that higher education students had the highest number of confirmed COVID-19 cases among all students during the Covid-19 pandemic, our survey indicated that approximately 84% of students in higher education did not have COVID19-related experiences, such as quarantine or self-monitored health management while being infected. Overall, largely due to the precautions taken by the government at the initial stages of the pandemic, the prevalence of COVID-19 in Taiwan has remained low.[<reflink idref="bib5" id="ref34">5</reflink>],[<reflink idref="bib29" id="ref35">29</reflink>] At the same time, however, the nationwide level-3 alert and lock-down for all campuses in Taiwan resulted in unexpected changes to higher education communities.</p> <p>Furthermore, the positive correlation between mental health and the impact of the COVID-19 confirmed the important role of counseling support for students in institutional environments. Our findings also suggest that COVID-19's effects on students' financial conditions, educational experiences, and social interactions have significantly impacted their levels of anxiety, loneliness, and depression. With regard to financial conditions, the working hours of students employed part-time off-campus were often reduced due to the pandemic, increasing their anxiety about meeting their financial needs.[<reflink idref="bib18" id="ref36">18</reflink>] For example, students in New York reported they and/or someone else in their household lost income as a result of the pandemic, and nearly half of the participants in this study reported their weekly household expenses increased. This result is consistent with prior research findings[<reflink idref="bib30" id="ref37">30</reflink>] that in China students' level of anxiety was associated with the degree of their family's economic stability. Educational experiences and social interactions during the pandemic were also found to be significantly associated with mental health outcomes, including high levels of anxiety, loneliness, and depression among students. Prior to the pandemic, college students in Taiwan as elsewhere were used to traditional on-site teaching in combination with different instructional methods, so a sudden switch to full-time distance learning not only jeopardized their learning engagement but also limited their social interactions. During the lockdown, all learning activities were shifted to virtual learning. The quality of students' learning experiences depended not only on the virtual learning environment provided but also on their access to equipment for remote learning as well as their skills in the use of digital media.[<reflink idref="bib8" id="ref38">8</reflink>],[<reflink idref="bib31" id="ref39">31</reflink>],[<reflink idref="bib32" id="ref40">32</reflink>] In addition, social distancing protocols during the pandemic interfered with students' social interactions with classmates, friends and family members, often leading to loneliness and lack of social support.[<reflink idref="bib33" id="ref41">33</reflink>] Therefore, universities and colleges should help to develop better targeted interventions to help students maintain quality in their close relationships and learning environment, especially those experiencing highly restrictive conditions.</p> <p>These findings further highlight the importance of social and emotional support from peers and families[<reflink idref="bib3" id="ref42">3</reflink>],[<reflink idref="bib29" id="ref43">29</reflink>] and are in line with recent studies demonstrating college students' stress specifically associated with COVID-19.[<reflink idref="bib9" id="ref44">9</reflink>],[<reflink idref="bib10" id="ref45">10</reflink>],[<reflink idref="bib22" id="ref46">22</reflink>] As restrictions related to Covid-19 continue to demand as well as other emergency remain active or on the horizon, online learning will be a necessity, and, in addition to enhancing student's awareness of essential health protection measures, providing the necessary mental and emotional support mechanisms and counseling services will be a major responsibility of universities and colleges.</p> <hd id="AN0181109497-15">Conclusion</hd> <p>To our knowledge, this study is the first effort to investigate the impact of the pandemic on a broad sample of students in higher education in Taiwan using the technique of machine learning. Findings suggest that the pandemic impacted students' financial conditions, educational experiences, and social interactions, which in turn were significantly associated with their mental health outcomes, while their demographic characteristics were not. This result might have occurred because the impact of COVID-19 on the student population was not as severe in Taiwan as in other countries. Also, Taiwan's Ministry of Education directed postsecondary institutions to develop continency plans in response to the pandemic, such as online teaching, to prevent cluster infection. Once in place, safety measures and guidelines decreased the possibility of disease transmission, which helps explain why some certain factors such as experiences with Covid-19 related were not found to be strongly related to mental health outcomes in this study. On the other hand, the findings suggest that restrictions related to COVID-19 have had a significant impact on students' financial conditions, educational experiences, and social interactions, mental health outcomes, which have had differential effects on students' anxiety, loneliness, and depression.</p> <hd id="AN0181109497-16">Limitations of the study</hd> <p>While the findings of this study provide important insights into the social and economic effects of a pandemic on students' mental health outcomes, some limitations should be noted. First, given the time constraints and the mode of data collection, the sample was not fully comprehensive across different types of institutions, suggesting caution in making generalizations to other populations in other settings. In future research, we recommend the use of a stratified nationwide sample. Second, we conducted this study in July 2021, in the midst of a lockdown soon after a spike of confirmed cases of COVID-19 was reported in Taiwan. At this time, students from higher education community were experiencing approximately three months of distance learning, during which all campus activities and courses were abruptly shifted to the online platform the until fall semester, suggesting that the full effects of the restrictions and disease risk had not yet materialized. Longitudinal studies of the effects of the pandemic on postsecondary students' mental health should be carried out with nationally representative samples to assess the full effects of changes imposed by this level of national health emergency.</p> <hd id="AN0181109497-17">Conflict of interest disclosure</hd> <p>The authors have no conflicts of interest to report. The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements, of Taiwan and received approval from the Institutional Review Board of Kaohsiung Medical University.</p> <hd id="AN0181109497-18">Ethical approval</hd> <p>This research is approved by the IRB of Kaohsiung Medical University, Taiwan (KMUHIRB-E(II)-20210178).</p> <ref id="AN0181109497-19"> <title> References </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Nurunnabi M, Almusharraf N, Aldeghaither D. Mental health and well-being during the COVID-19 pandemic in higher education: evidence from G20 countries. J Public Health Res. 2021; 9 (Suppl 1): 2010. doi: 10.4081/jphr.2020.2010.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">2</bibl> <bibtext> Izumi T, Sukhwani V, Surjan A, Shaw R. Managing and responding to pandemics in higher educational institutions: initial learning from COVID-19. 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  Data: Investigating Mental Health Outcomes of Undergraduates and Graduate Students in Taiwan during the COVID-19 Pandemic
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  Data: <searchLink fieldCode="AR" term="%22Ching-Hui+Lin%22">Ching-Hui Lin</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4785-2871">0000-0002-4785-2871</externalLink>)<br /><searchLink fieldCode="AR" term="%22Szu-Yin+Lin%22">Szu-Yin Lin</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5083-0932">0000-0001-5083-0932</externalLink>)<br /><searchLink fieldCode="AR" term="%22Bo-Hsien+Hu%22">Bo-Hsien Hu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1337-3036">0000-0003-1337-3036</externalLink>)<br /><searchLink fieldCode="AR" term="%22C%2E+Owen+Lo%22">C. Owen Lo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8294-9524">0000-0001-8294-9524</externalLink>)
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  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Stress+Variables%22">Stress Variables</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Pandemics%22">Pandemics</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22School+Closing%22">School Closing</searchLink><br /><searchLink fieldCode="DE" term="%22Interpersonal+Relationship%22">Interpersonal Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+Problems%22">Financial Problems</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Experience%22">Student Experience</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+Health%22">Mental Health</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Patterns%22">Psychological Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Anxiety%22">Anxiety</searchLink><br /><searchLink fieldCode="DE" term="%22Depression+%28Psychology%29%22">Depression (Psychology)</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Taiwan%22">Taiwan</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/07448481.2022.2162824
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0744-8481<br />1940-3208
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: This study is an exploration of the major stressors associated with the COVID-19 for students in higher education in Taiwan. Participants: The sample comprised 838 higher education students studying at various Taiwanese universities. Methods: A cross-sectional online survey was administered at different postsecondary institutions during the semi-lockdown period of COVID-19, which mandated online instruction. Machine learning was employed to determine the variables that most highly predicted students' mental health using R. Results: The findings revealed that COVID-19-related experiences, including social interactions, financial conditions, and educational experiences, were significantly associated with mental health outcomes. Particularly, loneliness are significantly related to social interactions and educational experiences. Conclusions: Findings revealed that Covid-19 impacted Taiwanese students' financial conditions, educational experiences, and social interactions, which were significant predictors of their mental health outcomes such as anxiety, loneliness and depression. The current study contributes to the gap in knowledge about mental health issues among postsecondary students during the pandemic.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1451923
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1451923
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/07448481.2022.2162824
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 3402
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Stress Variables
        Type: general
      – SubjectFull: COVID-19
        Type: general
      – SubjectFull: Pandemics
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: School Closing
        Type: general
      – SubjectFull: Interpersonal Relationship
        Type: general
      – SubjectFull: Financial Problems
        Type: general
      – SubjectFull: Student Experience
        Type: general
      – SubjectFull: Mental Health
        Type: general
      – SubjectFull: Psychological Patterns
        Type: general
      – SubjectFull: Anxiety
        Type: general
      – SubjectFull: Depression (Psychology)
        Type: general
      – SubjectFull: Taiwan
        Type: general
    Titles:
      – TitleFull: Investigating Mental Health Outcomes of Undergraduates and Graduate Students in Taiwan during the COVID-19 Pandemic
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ching-Hui Lin
      – PersonEntity:
          Name:
            NameFull: Szu-Yin Lin
      – PersonEntity:
          Name:
            NameFull: Bo-Hsien Hu
      – PersonEntity:
          Name:
            NameFull: C. Owen Lo
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 0744-8481
            – Type: issn-electronic
              Value: 1940-3208
          Numbering:
            – Type: volume
              Value: 72
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Journal of American College Health
              Type: main
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