Preliminary validation of a questionnaire assessing psychological distress in caregivers of patients with malignant mesothelioma: Mesothelioma Psychological Distress Tool—Caregivers

Abstract

Objective: To develop a short, flexible, and comprehensive tool to measure psychological distress in caregivers of malignant mesothelioma (MM) patients: the Mesothelioma Psychological Distress Tool-Caregivers version (MPDT-C).Methods: Based on a systematic review of the relevant literature, aspects associated with caregiver distress were derived. Expert researchers/clinicians developed a pool of items for each identified aspect. Content validity was assessed through a multiple mixed-methods approach. A multicenter study was conducted to explore the factorial structure of the 47-item MPDT-C through Bayesian factor analysis. Results:The Bayesian exploratory factor analysis revealed an underlying threefactor structure. Factors were labeled Secondary Traumatic Stress, Engagement in Caring, and Meaningful Cognitive Restructuring. All scales showed sufficient reliability and corrected item-total correlations. Females scored higher than males for Engagement in Caring. Conclusions:Taking care of malignant mesothelioma patients is a stressful process that influences the caregiver's physical, emotional, and social wellbeing. Our study offers preliminary evidence in support of the adequate psychometric properties of the MPDT-C, and these should now be replicated. Results suggest that the MPDT-C 122 -

Preliminary validation of a brief PROM assessing psychological distress in patients with malignant mesothelioma: The mesothelioma psychological distress tool—Patients

“…Our results (i.e., the posterior of the present analysis) could become the prior distribution for future research developments, by gradually reducing the uncertainty on the final inference (Rupp et al, 2004). Moreover, they also emphasize the utility of adopting a trauma-focused framework for conceptualizing MM-related psychological pain, as already proposed in the literature (Hawley and Monk, 2004;Guglielmucci et al, 2014;Granieri, 2016;Di Basilio et al, 2021;Bonafede et al, 2022).…”

“…In-depth qualitative studies of the subjective experience of patients living with MM found that they felt ashamed, blamed, and stigmatized for their illness (Hughes and Arber, 2008;Borgogno et al, 2015;Franzoi et al, 2015;Granieri et al, 2017). Even decades after asbestos exposure, self-blaming and "an unpleasant feeling with accompanying beliefs that one should have thought, felt, or acted differently" (i.e., guilt) may persist (Borgogno et al, 2015;Guglielmucci et al, 2018a;Bonafede et al, 2022).…”

“…Identifying underlying mental dynamics related to the traumatic impact of the disease is a crucial but hard process, worsened by the reluctance of MM patients to openly share their thoughts and express how they feel (Chapple et al, 2004;Granieri et al, 2017;Guglielmucci et al, 2018a;Girgis et al, 2019;Warby et al, 2019;Walker et al, 2021;Bonafede et al, 2022). Integrating the use of MPDT-P in clinical practice should help practitioners overcome this issue and obtain a quick and reliable picture of MM-related specific suffering.…”

Coefficients of Factor Score Determinacy for Mean Plausible Values of Bayesian Factor Analysis

“…However, there are cautionary notes on the use of default settings for convergence (Zitzmann & Hecht, 2019) or on the use of default prior distributions (Smid & Winter, 2020). Nevertheless, Bayesian factor analysis (BFA) is meanwhile regularly used (e.g., Bonafede et al, 2021; Wang et al, 2021; Weide et al, 2021). It follows from the regular use of BFA that plausible values as factor score estimates computed from BFA (Asparouhov & Muthén, 2010a, 2010b) may also be used regularly.…”

Correlation-Preserving Mean Plausible Values as a Basis for Prediction in the Context of Bayesian Structural Equation Modeling

“…Bayesian structural equation modeling (BSEM) has been proposed by Muthén and Asparouhov (2012) as an alternative to conventional structural equation modeling and several improvements of BSEM have meanwhile been proposed and realized (Asparouhov & Muthén, 2021;Asparouhov, Muthén, & Morin, 2015;Zitzmann & Hecht, 2019). Advantages of BSEM are that it can be performed with relatively small samples (Bonafede, Chiorri, Azzolina, 2021) and that priors for the variability of loadings can be specified. BSEM thereby allows to overcome problems of specifying fixed zero loadings in the independent clusters model (Beauducel & Hilger, 2020) and it also allows for the specification of complex loading patterns as, for example, circumplex models (Weide, Scheuble, & Beauducel, 2021).…”