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Record W6939716956 · doi:10.60692/bk6z1-89802

The psychological impact of COVID-19 and the subsequent social isolation on the general population of Karnataka, India

2020· article· en· W6939716956 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2020
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyMental healthPopulationSocial isolationDescriptive statisticsSnowball samplingDepression (economics)Psychological intervention

Abstract

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Background: The COVID-19 pandemic has various unfavorable effects on individuals and the community. This study aims to assess the psychological impact of the COVID-19 epidemic and the subsequent social isolation on the general population of Karnataka, India. Methods: A web-based cross-sectional survey was conducted in Karnataka from 8 to 14 April 2020 using the snowball technique. The psychological impact was assessed with the help of the nine-item Patient Health Questionnaire-9 (PHQ-9) and seven-item General Anxiety Disorder-7 (GAD-7) questionnaires. IBM SPSS Statistics Subscription version 16.0 was recruited to analyze the data. Descriptive (Mean + Standard Deviation) and bivariate (Pearson chi-square and ANOVA tests) analysis used to present data with the significance level set at less than 0.05. Results: This study included 1537 participants from 26 cities in Karnataka. About two-thirds of the respondents were undergraduate students (951, 61.9%), females (768, 50.0%), and 40.1% stayed about 15-20 days in social isolation. The prevalence of depression was 47.0%, and anxiety was 41.5%, respectively, among the surveyed sample. After the analysis, the age group 21-30 year old (P < 0.001), females P < 0.001), urban residents (P = 0.021), and the students (P p < 0.001) were significant for depression. However, only the age group 31-40 years was found to be more susceptible to anxiety. Conclusion: As important as addressing the psychological effects, knowing people at risk of developing mental illnesses will contribute effectively to providing appropriate psychological rehabilitation programs at the right time. References World Health Organization, Novel Coronavirus (2019-nCoV) Situation Report –1, 21 January 2020. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf, [Accessed on 30 August 2020]. Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, Ho RC. Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. Int J Environ Res Public Health. 2020 Mar 6;17(5):1729. https://doi.org/10.3390/ijerph17051729. World Health Organization, WHO Director-General's opening remarks at the media briefing on COVID-19 - 11 March 2020. Available from: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 [Accessed on 13 April 2020] Coronavirus in India: Latest Map and Case Count. Available from: https://www.covid19india.org/ [Accessed 13 April 2020]. Arakal RA. First COVID-19 case in Karnataka: Techie who returned to Bengaluru from US tests positive, (9 March2020). Available from: https://indianexpress.com/article/cities/bangalore/coronavirus-karnataka-first-case-covid-19-bengaluru-6307223/ [Accessed on 13 April 2020] India Today on 24 March 2020. Modi announces lockdown Updates: No panic buying please. Stay indoors, tweets PM. Available from: https://www.indiatoday.in/india/story/pm-modi-address-the-nation-at-8-pm-today-speech-covid-19-coronavirus-live-updates-1659215-2020-03-24 [Accessed on 13 April 2020] Ali Jadoo SA. Was the world ready to face a crisis like COVID-19? Journal of Ideas in Health2020;3(1):123-4. https://doi.org/10.47108/jidhealth.Vol3.Iss1.45 Steptoe A, Shankar A, Demakakos P, Wardle J. Social isolation, loneliness, and all-cause mortality in older men and women. Proc Natl Acad Sci U S A. 2013;110(15):5797-5801. https://doi.org/10.1073/pnas.1219686110 Cao W, Fang Z, Hou G, Han M, Xu X, Dong J, et al. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res. 2020; 287:112934. https://doi.org/10.1016/j.psychres.2020.112934 Taylor HO, Taylor RJ, Nguyen AW, Chatters L. Social Isolation, Depression, and Psychological Distress Among Older Adults. Journal of Aging and Health2018; 30(2): 229–246. https://doi.org/10.1177/0898264316673511 Sim K, Huak Chan Y, Chong PN, Chua HC, Wen Soon S. Psychosocial and coping responses within the community health care setting towards a national outbreak of an infectious disease. J Psychosom Res. 2010;68(2):195-202. https://doi.org/10.1016/j.jpsychores.2009.04.004 Roy D, Tripathy S, Kar SK, Sharma N, Verma SK, Kaushal V. Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during COVID-19 pandemic. Asian J Psychiatr. 2020; 51:102083. https://doi.org/10.1016/j.ajp.2020.102083. Karnataka Population. Available from: http://www.populationu.com/in/karnataka-population [Accessed on 8 April 2020] Sample Size Calculator: Understanding Sample Sizes. Available from: https://www.surveymonkey.com/mp/sample-size-calculator/ [Accessed on 5 March 2020] Toussaint A, Hüsing P, Gumz A, Wingenfeld K, Härter M, Schramm E, Löwe B. Sensitivity to change and minimal clinically important difference of the 7-item generalized anxiety disorder questionnaire (GAD-7). J Affect Disord. 2020; 265:395–401. https://doi.org/10.1016/j.jad.2020.01.032 Williams N. The GAD-7 Questionnaire [Review of the test Generalized anxiety disorder (gad-7) Questionnaire, by R. L. Spitzer]. Occupational Medicine2014; 64(3): 224. https://doi.org/10.1093/occmed/kqt161 Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606. Albert PR. Why is depression more prevalent in women? J Psychiatry Neurosci. 2015;40(4):219-221. https://doi.org/10.1503/jpn.150205 Patten SB, Wang JL, Williams JV, Wang JL, McDonald K, Bulloch ACM. Descriptive epidemiology of major depression in Canada. Can J Psychiatry. 2006; 51:84–90. https://doi.org/10.1177/070674371506000106 Jones C. Student anxiety, depression increasing during school closures, survey finds. EdSorce, 13 May 2020. Available from: https://edsource.org/2020/student-anxiety-depression-increasing-during-school-closures-survey-finds/631224 [Accessed on 29 August 2020]. Frasquilho D, Matos MG, Salonna F, Guerreiro D, Storti CC, Gaspar T, Caldas-de-Almeida JM. Mental health outcomes in times of economic recession: a systematic literature review. BMC Public Health2015; 16:115. https://doi.org/10.1186/s12889-016-2720-y. Ali Jadoo SA. COVID -19 pandemic is a worldwide typical Biopsychosocial crisis. Journal of Ideas in Health2020;3(2):152-4. https://doi.org/10.47108/jidhealth.Vol3.Iss2.58 Prabhu N. Bengaluru urban tops state in per capita income, Kalaburagi last, (20 March 2016). Available from: https://www.thehindu.com/news/cities/bangalore/bengaluru-urban-tops-state-in-per-capita-income-kalaburagi-last/article8376124.ece [Accessed 13 April 2020].

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.136
GPT teacher head0.388
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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