Psychometric evaluation of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) among adults with Long COVID, ME/CFS, and healthy controls: A machine learning approach.
Long COVID shares a number of clinical features with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), including post-exertional malaise, severe fatigue, and neurocognitive deficits. Utilizing validated assessment tools that accurately and efficiently screen for these conditions can facil...
| Publicado en: | Journal of Health Psychology Vol. 29; no. 11; pp. 1241 - 1253 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Sep2024
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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=ccm&AN=179737686&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179737686 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13591053 3FA jtl: Journal of Health Psychology issn: 13591053 maglogo: Y pubinfo: dt: Sep2024 vid: 29 iid: 11 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179737686 175016824 179737686 179737686 10.1177/13591053231223882 179737686 ppf: 1241 ppct: 12 formats: tig: atl: Psychometric evaluation of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) among adults with Long COVID, ME/CFS, and healthy controls: A machine learning approach. aug: au: McGarrigle, William J Furst, Jacob Jason, Leonard A affil: University of Kentucky, USA sug: subj: COVID-19 Psychosocial Factors COVID-19 Diagnosis Psychometrics Evaluation Random Forest Methods Fatigue Syndrome, Chronic Psychosocial Factors Machine Learning Prediction Models Human Funding Source Questionnaires Male Female Sensitivity and Specificity Post-Acute COVID-19 Syndrome Male Female ab: Long COVID shares a number of clinical features with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), including post-exertional malaise, severe fatigue, and neurocognitive deficits. Utilizing validated assessment tools that accurately and efficiently screen for these conditions can facilitate diagnostic and treatment efforts, thereby improving patient outcomes. In this study, we generated a series of random forest machine learning algorithms to evaluate the psychometric properties of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) in classifying large groups of adults with Long COVID, ME/CFS (without Long COVID), and healthy controls. We demonstrated that the DSQ-SF can accurately classify these populations with high degrees of sensitivity and specificity. In turn, we identified the particular DSQ-SF symptom items that best distinguish Long COVID from ME/CFS, as well as those that differentiate these illness groups from healthy controls. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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