Psychometric Validation of the Fear of COVID-19 Scale (FCV-19S) in a US Academic Health Sciences Center

The COVID-19 pandemic (2020–2023) profoundly disrupted healthcare systems and imposed sustained psychological burdens on healthcare professionals and trainees. Reliable instruments are essential for assessing these impacts. This study evaluated the construct validity and reliability of the Fear of COVID-19 Scale (FCV-19S) in a convenience sample of 1761 healthcare professionals, trainees, and academic staff at a single U.S. academic health sciences center (the University of Oklahoma Health Campus). Participants completed the FCV-19S; confirmatory factor analysis (CFA) examined its dimensional structure; and internal consistency was assessed using Cronbach’s α and McDonald’s ω. The one-factor model demonstrated good internal consistency (α = 0.89; ω = 0.89) but exhibited sub-optimal model fit (CFI = 0.89; TLI = 0.83; SRMR = 0.06; RMSEA = 0.18). The two-factor model, distinguishing emotional and somatic fear, showed substantially improved fit (CFI = 0.97; TLI = 0.96; SRMR = 0.03; RMSEA = 0.09) and acceptable internal consistency for both factors (α = 0.85 and 0.86; ω = 0.85 and 0.87), although RMSEA remained above conventional thresholds for close fit. Overall, findings support a two-factor structure as a comparatively superior and more nuanced representation of fear responses among an academic health workforce. The validated FCV-19S offers a reliable tool for assessing COVID-19-related distress in clinical and educational health settings, informing targeted interventions to strengthen workforce resilience.

Authors

Institutions

Publication Details

Journal
COVID
Published
2026-02-04
DOI
https://doi.org/10.3390/covid6020026
Primary Topic
COVID-19 and Mental Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Psychometric Validation of the Fear of COVID-19 Scale (FCV-19S) in a US Academic Health Sciences Center

Michael Brand, Gargi Deshpande, Brandt Wiskur, Blake T. Hilton et al.
COVID
COVID-19 and Mental Health
article

Psychometric Validation of the Fear of COVID-19 Scale (FCV-19S) in a US Academic Health Sciences Center

Michael Brand, Gargi Deshpande, Brandt Wiskur, Blake T. Hilton, Kelly A. Garrett, Maria Trapp, Kavya Boyina, Katrin Gaardbo Kuhn, Bijay Rimal
article en

Abstract

The COVID-19 pandemic (2020–2023) profoundly disrupted healthcare systems and imposed sustained psychological burdens on healthcare professionals and trainees. Reliable instruments are essential for assessing these impacts. This study evaluated the construct validity and reliability of the Fear of COVID-19 Scale (FCV-19S) in a convenience sample of 1761 healthcare professionals, trainees, and academic staff at a single U.S. academic health sciences center (the University of Oklahoma Health Campus). Participants completed the FCV-19S; confirmatory factor analysis (CFA) examined its dimensional structure; and internal consistency was assessed using Cronbach’s α and McDonald’s ω. The one-factor model demonstrated good internal consistency (α = 0.89; ω = 0.89) but exhibited sub-optimal model fit (CFI = 0.89; TLI = 0.83; SRMR = 0.06; RMSEA = 0.18). The two-factor model, distinguishing emotional and somatic fear, showed substantially improved fit (CFI = 0.97; TLI = 0.96; SRMR = 0.03; RMSEA = 0.09) and acceptable internal consistency for both factors (α = 0.85 and 0.86; ω = 0.85 and 0.87), although RMSEA remained above conventional thresholds for close fit. Overall, findings support a two-factor structure as a comparatively superior and more nuanced representation of fear responses among an academic health workforce. The validated FCV-19S offers a reliable tool for assessing COVID-19-related distress in clinical and educational health settings, informing targeted interventions to strengthen workforce resilience.

COVIDVol. 6(2)
Harvard University (US), McLean Hospital (US), University of Oklahoma Health Sciences Center (US), University of Oklahoma (US)
Openalex Percentile: Top 68%
COVID-19 and Mental Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.