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Revisiting dimensionality in measurement of sense of coherence among rural healthcare workers using network analysis
by
Skinner, Timothy
, Santiago, Pedro Henrique Ribeiro
, Cordon, Emma L.
, McEvoy, Anne
, Chan, Carina
, Crombie, Angela
, McEvoy, Mark
, Stanyer, Evan
, Masman, Kevin
, Begg, Stephen
, Bamforth, Laura
, Faulkner, Peter
, Li, Xia
in
631/477
/ 692/700/478
/ COVID-19
/ Dimensionality
/ Discriminant analysis
/ Eigenvalues
/ Health care
/ Health promotion
/ Healthcare workers
/ Humanities and Social Sciences
/ Mallee
/ Medical personnel
/ Mental health
/ multidisciplinary
/ Network psychometrics
/ Pandemics
/ Quantitative psychology
/ Rasch model
/ Resilience (Psychology)
/ Rural
/ Salutogenic
/ Science
/ Science (multidisciplinary)
/ SOC-13
/ Validity
/ Well being
/ Workforce
2026
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Revisiting dimensionality in measurement of sense of coherence among rural healthcare workers using network analysis
by
Skinner, Timothy
, Santiago, Pedro Henrique Ribeiro
, Cordon, Emma L.
, McEvoy, Anne
, Chan, Carina
, Crombie, Angela
, McEvoy, Mark
, Stanyer, Evan
, Masman, Kevin
, Begg, Stephen
, Bamforth, Laura
, Faulkner, Peter
, Li, Xia
in
631/477
/ 692/700/478
/ COVID-19
/ Dimensionality
/ Discriminant analysis
/ Eigenvalues
/ Health care
/ Health promotion
/ Healthcare workers
/ Humanities and Social Sciences
/ Mallee
/ Medical personnel
/ Mental health
/ multidisciplinary
/ Network psychometrics
/ Pandemics
/ Quantitative psychology
/ Rasch model
/ Resilience (Psychology)
/ Rural
/ Salutogenic
/ Science
/ Science (multidisciplinary)
/ SOC-13
/ Validity
/ Well being
/ Workforce
2026
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Revisiting dimensionality in measurement of sense of coherence among rural healthcare workers using network analysis
by
Skinner, Timothy
, Santiago, Pedro Henrique Ribeiro
, Cordon, Emma L.
, McEvoy, Anne
, Chan, Carina
, Crombie, Angela
, McEvoy, Mark
, Stanyer, Evan
, Masman, Kevin
, Begg, Stephen
, Bamforth, Laura
, Faulkner, Peter
, Li, Xia
in
631/477
/ 692/700/478
/ COVID-19
/ Dimensionality
/ Discriminant analysis
/ Eigenvalues
/ Health care
/ Health promotion
/ Healthcare workers
/ Humanities and Social Sciences
/ Mallee
/ Medical personnel
/ Mental health
/ multidisciplinary
/ Network psychometrics
/ Pandemics
/ Quantitative psychology
/ Rasch model
/ Resilience (Psychology)
/ Rural
/ Salutogenic
/ Science
/ Science (multidisciplinary)
/ SOC-13
/ Validity
/ Well being
/ Workforce
2026
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Revisiting dimensionality in measurement of sense of coherence among rural healthcare workers using network analysis
Journal Article
Revisiting dimensionality in measurement of sense of coherence among rural healthcare workers using network analysis
2026
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Overview
The COVID-19 pandemic adversely impacted the mental health and wellbeing of healthcare workers (HCW). Drawn from the theory of salutogenesis, Sense of Coherence (SOC) assesses the extent to which individuals perceive their life experiences as comprehensible, manageable, and meaningful, and is considered an important health promoting resource for HCWs’ resilience and coping under stressful circumstances. However, little is known about its measurement in HCW populations, and the current discrepancy regarding SOC dimensionality continues to limit salutogenic research and application. Previous studies have used factor analytic, Rasch and SEM approaches for assessing SOC dimensionality, which have since been challenged for accuracy. This study applied a novel psychometric methodology, namely Exploratory Graph Analysis (EGA), to revisit the dimensionality of the SOC-13 scale in a regional and rural Australian HCW cohort at 12-month follow-up (
n
= 649; complete SOC-13 cases
n
= 597), broadly representative of the Loddon Mallee healthcare workforce demographics. Results support a refined 12-item, unidimensional SOC instrument, achieved by removing one redundant item (SOCQ2) highly overlapping in content with SOCQ3, and demonstrated strong internal consistency and validity related to depression, anxiety, and resilience. Further research is needed to explore dimensionality across populations and to improve missing data treatment in network analyses.
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