Chat with us, powered by LiveChat Review the Learning Resources on using the library. Choose a topic from the list below: COVID-19 Childhood obesity Pancreatic cancer Falls - EssayAbode

Review the Learning Resources on using the library. Choose a topic from the list below: COVID-19 Childhood obesity Pancreatic cancer Falls


  • Review the Learning Resources on using the library.
  • Choose a topic from the list below:
    • COVID-19
    • Childhood obesity
    • Pancreatic cancer
    • Falls in the elderly
    • Health effects of climate change
  • Using the Walden library, find a scholarly, peer-reviewed article on the topic that is no more than 5 years old.
  • Find a media source on the topic that is no more than 1 year old. The media article should be written for the general public.
  • Review the rubric for this Assignment.
  • Review both the article and media source and consider their similarities and differences in how they report on the same topic.


  • Explain the process you used for finding the media article.
  • Summarize the media article and its findings.
  • Explain the process you used for finding the peer-reviewed article in the Walden library and how you determined it was peer reviewed.
  • Summarize the peer-reviewed article and its findings.
  • Compare the quality of the information from the peer-reviewed article to the mainstream media article. Note similarities and differences. How is each type of resource unique?
  • Explain the issues with using non-scholarly sources in your research.

Pivotal points in the COVID-19 pandemic – 5 essential reads Mascarelli, Amanda

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ABSTRACT (ENGLISH) […]the World Health Organization’s announcement on May 5 that it was ending the COVID-19 public health emergency of international concern that had been in place since January 2020 is indicative that the pandemic has entered a new chapter. An outbreak is a small but unusual increase in the expected number of cases of a given disease, while the term epidemic is used when an infectious disease outbreak is getting bigger and spreading over a broader geographic area. Health care experts and the media made frequent comparisons between the two, pointing to similarities in attitudes about mask-wearing and school closures as well as in the patterns of disease waves, spikes and surges. FULL TEXT Experts have made it clear that the end of the COVID-19 national emergency, which was lifted on May 11, 2023, does not mean an end to the pandemic. But this shift signals a remarkable turning point in a pandemic that is well into its fourth year –something that few could have imagined when the U.S. national emergency went into effect in March 2020. Likewise, the World Health Organization’s announcement on May 5 that it was ending the COVID-19 public health emergency of international concern that had been in place since January 2020 is indicative that the pandemic has entered a new chapter. It’s daunting to look back at our coverage and narrow it down to just a handful of standout stories amid all the twists and turns of the pandemic. But here are five stories from The Conversation’s archives that resonated with us, written by scholars who helped to illuminate complex issues at pivotal moments in the pandemic. 1. A whole new vocabulary It’s a little hard to remember the days when words like pandemic, endemic diseases, mRNA, variant and spike proteins were not a part of our vernacular or everyday conversations. But I vividly recall the day that the COVID-19 pandemic was declared and a friend asked me “What exactly is a pandemic?” It turns out a lot of people were asking that question and wondering about the difference between an outbreak of an infectious disease, an epidemic and a pandemic. Rebecca S.B. Fischer, an assistant professor of epidemiology at Texas A&M University, put it in straightforward terms: An outbreak is a small but unusual increase in the expected number of cases of a given disease, while the term epidemic is used when an infectious disease outbreak is getting bigger and spreading over a broader geographic area. A pandemic, on the other hand, is used when a disease is “international and out of control.” She went on to say that some epidemiologists reserve the term pandemic for when a disease is being sustained in newly affected regions through local transmission –a good characterization of the state of COVID-19 in March 2020. ———— Read more: What's the difference between pandemic, epidemic and outbreak? ———— 2. Comparisons to the 1918 flu ran rife From the earliest days of the COVID-19 pandemic, it was impossible to miss the haunting similarities between it and

the 1918 flu pandemic, which led to at least 50 million deaths worldwide between 1918 and 1920. Health care experts and the media made frequent comparisons between the two, pointing to similarities in attitudes about mask- wearing and school closures as well as in the patterns of disease waves, spikes and surges. But while the two once-in-a-century events have shared plenty of likenesses, the comparison also sometimes led to public misunderstandings about how the COVID-19 pandemic could play out, wrote historian Mari Webel and pediatric infectious disease specialist Megan Culler Freeman, both from the University of Pittsburgh. They explain that key differences in the sociopolitical context of the 1918 flu period, as well as marked differences between the virology behind the two diseases, set the 1918 flu and COVID-19 on different paths. “People seek answers from the experiences of influenza in 1918-19 for a fundamental reason: It ended.” ———— Read more: Compare the flu pandemic of 1918 and COVID-19 with caution –the past is not a prediction ———— 3. How and when pandemics end In late 2020, people were naturally wondering when and how the COVID-19 pandemic would end, and how we would know it was over. Nükhet Varlik, a historian from Rutgers University who studies disease, medicine and public health, wrote an astute piece in October 2020 about the difficulties of predicting how the pandemic might play out. She presciently noted that “whether bacterial, viral or parasitic, virtually every disease pathogen that has affected people over the last several thousand years is still with us, because it is nearly impossible to fully eradicate them.” These include diseases like tuberculosis, leprosy, measles and plague. “Hopefully COVID-19 will not persist for millennia,” Varlik wrote. But she went on to say that politics are crucial, noting how when vaccination programs are weakened, infections can “come roaring back.” “Given such historical and contemporary precedents, humanity can only hope that the coronavirus that causes COVID-19 will prove to be a tractable and eradicable pathogen. But the history of pandemics teaches us to expect otherwise.” ———— Read more: How do pandemics end? History suggests diseases fade but are almost never truly gone ———— 4. The midway point The summer of 2021 felt like a particularly grueling moment in time –when excitement and optimism over the launch of the first vaccines to protect against COVID-19 had given way to despair over the stronghold of vaccine resistance and general exhaustion with all things COVID. And then came the delta variant. Epidemiologist Katelyn Jetelina from the University of Texas Health Science Center at Houston captured 18 months of the COVID-19 pandemic in a series of seven retrospective charts that put all of the high and low points into stark relief. “The race between vaccination and variant spread was upon us,” Jetelina wrote. “The fight was far from over.” The same may still be true today. ———— Read more: 18 months of the COVID-19 pandemic –a retrospective in 7 charts ———— 5. How omicron altered the course of the pandemic When the omicron variant arrived on the scene in late 2021 and spread globally in early 2022, it soon became clear that it could bring about a shift in the pandemic. With its ability to spread easily and to also cause milder disease than prior variants, omicron had the potential to act as a natural vaccine of sorts –producing widespread immunity with the help of the existing COVID-19 vaccines. But the omicron variant had plenty of surprises in store. For one, it gave rise to a family of variants and sublineages that to this day are keeping researchers guessing, with the latest omicron subvariant, XBB.1.16, gaining ground across the U.S. and worldwide as of mid-May 2023.

In January 2022, immunology researchers Prakash Nagarkatti and Mitzi Nagarkatti, from the University of South Carolina, explained how the immune system responds to infections and how it remembers those threats through “immunological memory.” This left room for hope, they wrote, that “when new variants of SARS-CoV-2 inevitably arise, omicron will have left the population better equipped to fight them. So the COVID-19 vaccines combined with the omicron variant could feasibly move the world to a new stage in the pandemic –one where the virus doesn’t dominate our lives and where hospitalization and death are far less common.” ———— Read more: Is the omicron variant Mother Nature’s way of vaccinating the masses and curbing the pandemic? ———— Editor’s note: This story is a roundup of articles from The Conversation’s archives. AuthorAffiliation Amanda Mascarelli, Senior Health and Medicine Editor DETAILS

Subject: Influenza; Infectious diseases; Public health; Archives &records; Pathogens; COVID- 19 vaccines; Severe acute respiratory syndrome coronavirus 2; Coronaviruses; Pandemics; Epidemics; Disease transmission

Location: United States–US

Publication title: The Conversation : COVID-19; Boston

Publication year: 2023

Publication date: May 17, 2023

Publisher: The Conversation US, Inc.

Place of publication: Boston

Country of publication: United States, Boston

Publication subject: Health Facilities And Administration, Medical Sciences

Source type: Newspaper

Language of publication: English

Document type: News

ProQuest document ID: 2814576595

Document URL: essential/docview/2814576595/se-2?accountid=14872

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Copyright: © 2023. This work is published under (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Last updated: 2023-05-18

Database: ProQuest One Academic,Coronavirus Research Database

  • Pivotal points in the COVID-19 pandemic – 5 essential reads


Research Article The Anxiety and Depression of International Medical Students during COVID-19 Pandemic: A Cross-Sectional Study

Xiaoqing Chen 1 and Hong Sun 2

1Department of Student Afairs, Xuzhou Medical University, Xuzhou, Jiangsu, China 2Physiology Department, Xuzhou Medical University, Xuzhou, Jiangsu, China

Correspondence should be addressed to Hong Sun; [email protected]

Received 6 June 2023; Revised 25 February 2024; Accepted 4 March 2024; Published 12 March 2024

Academic Editor: Francesco Bartoli

Copyright © 2024 Xiaoqing Chen and Hong Sun. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in anymedium, provided the original work is properly cited.

Te COVID-19 pandemic has inficted physical harm and exacerbated a signifcant mental health crisis, warranting greater attention. Tis study investigated the prevalence of anxiety and depression among international medical students (IMSs) during the pandemic and explored its correlation with demographic factors. Participants completed a comprehensive questionnaire encompassing demographic details, the Zung self-rating anxiety scale, and the Zung self-rating depression scale. Te fndings revealed that 23.27% of IMSs reported anxiety, while 48.52% experienced symptoms of depression. Multivariate logistic regression analysis identifed poor health conditions and limited access to the family as independent risk factors for anxiety. At the same time, depression was associated with both compromised health and notable fnancial burdens. Tis study provides crucial insights for policymakers, college administrators, and government authorities, urging proactive measures to support and manage the wellbeing of IMSs during pandemic situations.

1. Introduction

Te outbreak of large-scale epidemics, such as the MERS epidemic in the Middle East in 2012 and the Ebola outbreak inWest Africa in 2014, poses not only a risk of death but also exacerbates mental health issues [1]. Research suggests that the physiological damage caused by these public health emergencies can be recovered quickly, but the psychological efects may persist longer [2]. Terefore, it is crucial to provide early psychological intervention to the public. In 2020, the COVID-19 outbreak was declared a pandemic by the World Health Organization (WHO) [3, 4], with sub- sequent reports highlighting adverse efects on the mental well-being of the general population [5]. Additionally, the pandemic has signifcantly afected various sectors, notably higher education, thereby infuencing the performance of students and aggravating social challenges they faced [6, 7]. Also, colleges implemented lockdown measures and shifted to online teaching to prevent the spread of COVID-19 to campuses [8]. However, these measures amplifed the

psychological burden on students, leading to increased levels of anxiety, depression, and stress [9–12]. Moreover, COVID-19 restrictions contributed to their uncertainty about the future [13, 14], and improper management of personal time and space [6, 15]. All these conditions, plus inadequate communication between students and in- stitutions further exacerbated mental health issues [16, 17].

Te international medical students (IMSs), as a special group, were among the most vulnerable categories [18]. During the pandemic, some IMSs were confned to campus, while others were stuck in their homelands as many countries prohibited foreigners from entering in order to avoid cross infection [19]. Numerous studies have docu- mented that medical students experienced more anxiety and depression compared to their peers in other majors due to massive academic pressure and other factors [20–22]. Furthermore, the suspension of clinical practice and labo- ratory activities during the pandemic further disrupted their educational process and daily routines [23]. As future healthcare professionals, medical students also grappled

Hindawi Perspectives in Psychiatric Care Volume 2024, Article ID 2285597, 9 pages

with the added stress of potential frontline involvement in pandemic response eforts. Terefore, their mental health deserved more attention [24]. However, little research on the mental health status of IMSs facing the pandemic has been conducted so far [25].

2. Methods

2.1. Design and Participants. Tis cross-sectional, online questionnaire-based study for IMSs at Xuzhou Medical University (China) was done from June 16, 2020 to June 23, 2020. Participants were eligible to participate if they were international students of Xuzhou Medical University who had not been diagnosed with psychological or mental ill- nesses before the pandemic. Students meeting the inclusion criteria received a message about the study in class online groups.Tey were informed about the study’s goal, and their participation was entirely optional. Te questionnaires were completed anonymously, and confdentiality was ensured. Te study was authorized by Xuzhou Medical University’s Ethics Committee and was carried out following the Helsinki Declaration (as revised in 2013). Consent from each student was obtained at the beginning of the online survey.

2.2. Research Tools. Te self-report questionnaire used in this study comprised three sections, including demographic information, the Zung Self-Rating Anxiety Scale (Zung SAS), and the Zung self-rating depression Scale (Zung SDS).

2.2.1. General Condition Questionnaire. Te general con- dition questionnaire consisted of demographic information of IMSs, including gender, age, grade, major, marital status, fnancial burden, health condition, physical exercise, time spent on study per day, the continent of origin, current location, clinical practice experience, and protective equipment condition for the pandemic.

2.2.2. Te Zung Self-Rating Anxiety Scale (Zung SAS). Te psychological anxiety among IMSs was measured via the SAS, a 20-item self-report scale with items rated on a 4-point Likert scale (from 1 “a little of the time” to 4 “most of the time”), which was developed by Zung in 1971 [26]. Te total score of each item is the raw score, which is then multiplied by 1.25 to obtain the standard score. Te cutofs for the SAS standard scores were classifed as less than 50, no anxiety; 50–59, minimal to mild anxiety; 60–69, moderate to marked anxiety; and greater than 70, severe anxiety [27].

2.2.3. Te Zung Self-Rating Depression Scale (Zung SDS). Te SDS is a self-rating scale with 20 questions assessing emotional symptoms in the past week. Each item is scored on a Likert scale that ranges from 1 to 4 according to the frequency of symptoms in the past 7 days. Te score from each question is calculated to obtain the raw score, and the standard score is equivalent to the raw score multiplied by 1.25. A standard score ≥50 indicates “psychological de- pression”; 50–59, minimal to mild depression; 60–69,

moderate to marked depression; and greater than 70, severe depression. [28] In this study, the SDS scale was used to evaluate the prevalence and level of depression of IMSs [27].

2.2.4. Statistical Analysis. Statistical analysis was performed using the Statistical Package for Social Science (SPSS Inc., version 22.0, IBM). Descriptive analysis was used to describe the status of anxiety and depression and the distributions of the demographic characteristics of students. Te SAS and SDS scores were expressed as mean± standard deviation. After the normality test of variables, a t-test or ANOVA was used if meeting the parametric test requirements. Otherwise, non-parametric tests (Mann–Whitney U-test) were per- formed as appropriate. A t-test or ANOVA was used for normally distributed data, whereas a Mann–Whitney U-test was applied for non-normally distributed data. All de- mographic data were analyzed and presented as frequency and percentage. A univariate analysis (chi-squared test) and multivariate logistic regression analysis were used to explore the associations between sample characteristics and anxiety as well as depression symptoms. Test level α� 0.05, P< 0.05 was considered statistically signifcant.

3. Results

3.1. Distributions of Anxiety and Depression Scores among IMSs during the COVID-19. In the current study, a total of 211 students completed the questionnaire. At the end, 202 questionnaires (95.73%) were fully flled out and considered valid. Among them, 57 students were in China, 145 were in their home countries, 131 were Asian, and 71 were from Africa. Levels of anxiety and depression of participants are demonstrated in Table 1. Te prevalence rates of anxiety and depression symptoms among the IMSs in this study were 23.27% (SAS index score ≥50) and 48.52% (SDS index score ≥50), respectively, though the mean scores of SAS and SDS indexes were 46.9± 7.7 points and 51.9± 10.1 points sepa- rately. For anxiety and depression status, the prevalence of each classifcation was 12.87% and 21.29% (minimal to mild), 6.93% and 19.31% (moderate to marked), and 3.47% and 7.92% (severe).

3.2. Te Diferences between the Demographic Variables and Anxiety as Well as Depression among IMSs during the COVID-19 Pandemic. Anxiety and depression scores were related to diferent demographic characteristics of students, which are presented in Table 2. Students with fnancial burdens were more prone to be anxious and depressed than those with good fnancial situations (P � 0.002; P � 0.001). However, there was no signifcant diference in anxiety and depression levels by sex, age, major, and marital status. In addition, poor health condition was associated with more anxiety (P< 0.001) and depression (P< 0.001). Also, short exercise and study time had a signifcant efect on anxiety (P � 0.044; P � 0.038) and depression (P � 0.011; P � 0.007). IMSs with short exercise and study time had increased anxiety and depression, respectively. However, clinical practice experience had no signifcant diference on

2 Perspectives in Psychiatric Care

Table 2: Diferences in anxiety and depression based on students’ demographic characteristics.

Variables Total SAS (x± s) Statistics P value SDS (x± s) Statistics P value Gender 4692.5 0.494a 4287 0.094a

Male 85 (42.08) 43.324± 1.613 48.165± 1.646 Female 117 (57.92) 42.863± 1.051 51.529± 1.247

Age 0.183 0.908d 0.784 0.504d

≤18 5 (2.48) 44.000± 4.766 45.000± 6.580 19–21 96 (47.52) 43.581± 1.449 51.604± 1.477 22–24 82 (40.59) 42.241± 1.308 48.988± 1.559 >24 19 (9.41) 43.684± 2.720 48.790± 3.248

Major −1.785 0.076c −0.391 0.696c

MBBS 186 (92.08) 42.567± 0.922 49.994± 1.021 Nursing 16 (7.92) 48.382± 3.890 51.412± 4.491

Marital status 3846 0.070a 4297.5 0.510a

Single 134 (66.34) 41.595± 0.996 49.508± 1.151 Have boyfriend/girlfriend 68 (33.66) 45.938± 1.819 51.309± 1.945

Financial situation 6.878 0.002b 6.758 0.001d

Good 42 (20.79) 38.631± 1.681 43.952± 2.093 Normal 123 (60.89) 42.449± 0.993 50.675± 1.189 Poor 37 (18.32) 50.101± 2.901 55.243± 2.690

Health condition 23.341 <0.001b 15.963 <0.001b Good 161 (79.70) 40.512± 0.823 47.926± 1.017 Normal 36 (17.82) 50.556± 2.110 56.806± 2.218 Poor 5 (2.48) 71.000± 3.337 72.400± 5.844

Physical exercise length per day 2.754 0.044d 3.820 0.011d

0 48 (23.76) 47.240± 2.241 55.125± 2.326 0–0.5 h 79 (39.11) 42.453± 1.138 50.430± 1.533 0.5–1 h 58 (28.71) 40.216± 1.567 45.983± 1.691 >1 h 17 (8.42) 43.750± 2.770 48.588± 3.097

Study length per day 8.449 0.038b 12.060 0.007b

≤2 h 40 (19.80) 49.469± 2.922 58.175± 2.907 2–4 h 89 (44.06) 41.924± 1.097 48.888± 1.276 4–6 h 45 (22.28) 42.333± 1.635 49.156± 1.907 >6 h 28 (13.86) 38.660± 2.064 43.036± 2.313

Continent of origin 4086.5 0.208a −1.990 0.048a

Asian countries 131 (64.85) 41.748± 0.984 48.703± 1.138 African countries 71 (35.15) 45.335± 1.819 52.887± 1.942

Current location 3086.500 0.005a 3.032 0.003a

China 57 (28.22) 48.114± 2.155 54.877± 2.203 Homeland 145 (71.78) 41.069± 0.895 48.241± 1.065

Clinical practice experience 0.777 0.438a −0.318 0.751a

Yes 32 (15.84) 44.688± 1.999 49.375± 2.177 No 170 (84.16) 42.750± 1.014 50.253± 1.123

Protective equipment sufciency 11.719 0.008b 15.727 0.001b

Sufcient 92 (45.54) 39.837± 1.094 45.620± 1.273 Basically sufcient 83 (41.09) 44.111± 1.226 52.675± 1.392 Insufcient 20 (9.90) 46.438± 3.296 54.550± 3.492 Terribly insufcient 7 (3.47) 63.214± 11.005 66.193± 10.936

at-test. bANOVA. cMann–Whitney test. dKruskal–Wallis test.

Table 1: Number of students with diferent levels of anxiety and depression (n� 202).

Level n (%)

Anxiety Depression Normal (range ≤50) 155 (76.73) 104 (51.48) Minimal to mild (range, 50–59) 26 (12.87) 43 (21.29) Moderate to marked (range, 60–69) 14 (6.93) 39 (19.31) Severe (range ≥70) 7 (3.47) 16 (7.92) n: number.

Perspectives in Psychiatric Care 3

student’s anxiety or depression. Furthermore, those who stayed on campus and those with insufcient protective equipment were more likely to report anxiety (P � 0.005; P � 0.008) and depression (P � 0.003; P � 0.001). Although no signifcant diferences were found in the continent of origin with respect to symptoms of anxiety, African students did show greater symptoms of depression as compared to Asian students (P � 0.048).

3.3. Factors Infuencing IMSs’ Anxiety and Depression during the COVID-19 Pandemic. After performing a univariate analysis for factors according to the personal number of IMSs’ anxiety and depression (Table 3), multivariate logistic regression analysis was used to further investigate the factors that infuenced students’ anxiety and depression during the COVID-19 crisis (Table 4). Te results indicated that two independent variables signifcantly predicted anxiety symptoms: health condition (odds ratio� 3.264, p � 0.001) and current location (odds ratio� 0.202, p � 0.024). Addi- tionally, fnancial situation (odds ratio� 1.892, p � 0.010) and health condition (odds ratio� 2.365, p � 0.015) are independent predictors of students’ depression symptoms.

3.4. Correlation between IMSs’ Anxiety and DepressionWhen Facing thePandemic. Another fnding of the study suggested a signifcant positive correlation between anxiety and de- pression (Table 5). Tis fnding is supported by previous studies which revealed that people with high anxiety were more likely to be more depressed [29]. And among the general population, major depressive disorders were highly comorbid with various anxiety disorders [30].

4. Discussion

Te fndings of the survey indicated that the pandemic af- fected 23.27% and 48.52% of IMS with anxiety and de- pression respectively. Tis fgure is lower than the percentage reported in earlier studies of Chinese college students, which was 41.1% and 60.5% for anxiety and de- pression, respectively [31, 32]. Tis diference may be be- cause most of the international students at Xuzhou Medical University come fromWest Asia and Africa and have several siblings. In contrast, most Chinese college students are only children. Siblings can provide social support and help with emotional regulation, whereas only children may experience more anxiety and depression due to a lack of social support [33]. Additionally, cross-cultural experiences can activate and further enhance their adaptability and facilitate adap- tation to new environments, which enabled IMSs to better cope with the pandemic’s sudden changes [34]. All these reasons may lead to lower rates of anxiety and depression compared to local Chinese students during the pandemic.

Te survey revealed that the anxiety of IMSs regarding this pandemic was related to their fnancial situation, health condition, length of exercise and study every day, current location and sufciency of protective equipment. On the other hand, the fnancial sit

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