The Epidemic–Pandemic Impacts Inventory (EPII): A Multisample Study Examining Pandemic-Related Experiences and Their Relation to Mental Health.
The Epidemic–Pandemic Impacts Inventory (EPII) was developed to assess pandemic-related adverse and positive experiences across several key domains, including work/employment, home life, isolation, and quarantine. Several studies have associated EPII-assessed pandemic-related experiences with a wide...
| Publicado en: | Psychological Assessment Vol. 35; no. 11; pp. 1019 - 1030 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Nov2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=173270815&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 173270815 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10403590 POL jtl: Psychological Assessment issn: 10403590 maglogo: N pubinfo: dt: Nov2023 vid: 35 iid: 11 pid: 34 pub: American Psychological Association artinfo: ui: 173270815 10.1037/pas0001248 ppf: 1019 ppct: 11 formats: tig: atl: The Epidemic–Pandemic Impacts Inventory (EPII): A Multisample Study Examining Pandemic-Related Experiences and Their Relation to Mental Health. aug: au: Janssen, Tim McGuire, Austen B. López-Castro, Teresa Prince, Mark A. Grasso, Damion J. affil: Center for Alcohol and Addiction Studies, School of Public Health, Brown University Department of Psychology, Dole Human Development Center, Clinical Child Psychology Program, University of Kansas Department of Psychology, The City College of New York, The City University of New York Department of Psychology, Colorado State University Department of Psychiatry, University of Connecticut School of Medicine su: Mental depression risk factors Work environment Mental health Experience Employment Social classes Anxiety Sociodemographic factors COVID-19 pandemic Academic medical centers Machine learning Random forest algorithms Regression analysis Risk assessment Descriptive statistics Prediction models Algorithms sug: subj: Mental depression risk factors Work environment Mental health Experience Employment Social classes Anxiety Sociodemographic factors COVID-19 pandemic Offices of Mental Health Practitioners (except Physicians) Academic medical centers Machine learning Random forest algorithms Regression analysis Risk assessment Descriptive statistics Prediction models Algorithms keyword: COVID-19 Epidemic–Pandemic Impacts Inventory machine learning measurement COVID-19 Epidemic–Pandemic Impacts Inventory machine learning measurement ab: The Epidemic–Pandemic Impacts Inventory (EPII) was developed to assess pandemic-related adverse and positive experiences across several key domains, including work/employment, home life, isolation, and quarantine. Several studies have associated EPII-assessed pandemic-related experiences with a wide range of psychosocial factors, most commonly depressive and anxiety symptoms. The present study investigated the degree to which specific types of COVID-19 pandemic-related experiences may be associated with anxiety and depression risk, capitalizing on two large, independent samples with marked differences in sociodemographic characteristics. The present study utilized two adult samples: participants (N = 635) recruited online over a 4-week period in early 2020 (Sample 1) and participants (N = 908) recruited from the student body of a large Northeastern public university (Sample 2). We employed a cross-validated, least absolute shrinkage and selection operator (LASSO) regression approach, as well as a random forest (RF) machine learning algorithm, to investigate classification accuracy of anxiety/depression risk using the pandemic-related experiences from the EPII. The LASSO approach isolated eight items within each sample. Two items from the work/employment and emotional/physical health domains overlapped across samples. The RF approach identified similar items across samples. Both methods yielded acceptable cross-classification accuracy. Applying two analytic approaches on data from two large, sociodemographically unique samples, we identified a subset of sample-specific and nonspecific pandemic-related experiences from the EPII that are most predictive of concurrent depression/anxiety risk. Findings may help to focus on key experiences during future public health disasters that convey greater risk for depression and anxiety symptoms. Public Significance Statement: This research provides a tool and data to assist efforts aimed at reducing risk of depression and anxiety associated with epidemic- or pandemic-related experiences in the context of future public health disasters. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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