Abstract
Objectives: Anticipatory grief (AG) is the process of experiencing loss prior to the death of a significant person. Coping with this multifaceted experience in the context of dementia caregiving is a relatively novel, yet significant area in caregiving literature. The Marwit-Meuser Caregiver Grief Inventory (MM-CGI) and its abbreviated MM-CGI-Short-Form (MM-CGI-SF) is the most widely used scale measuring AG. However, limited research has employed robust analytical strategies to assess its dimensional structure. This study employed contemporary factor analytical techniques to assess the dimensional structure of the MM-CGI / SF.
Method: Caregivers of persons with dementia (n = 508) completed a survey containing MM-CGI / SF and other associated psychological measures. Exploratory factor analysis was employed to compare eight alternative factor analytical models to determine the optimal model. Internal-consistency reliability was assessed by Cronbach’s α and construct validity was assessed by Spearman’s correlation-coefficient.
Results: The best fitting model was the MM-CGI-SF three factor model (Personal Sacrifice and Burden, Heartfelt Sadness and Longing and Worry and Felt Isolation). The MM-CGI-SF three factor model demonstrated internal consistency reliability and factor correlations with associated psychological measures indicated construct validity.
Conclusion: The MM-CGI-SF three factor model demonstrated adequate fit and utility, however, the Worry and Felt Isolation subscale needs further replication and revision to assess its dimensionality. The MM-CGI-SF is the more useful tool due to its brevity and better model fit.
Method: Caregivers of persons with dementia (n = 508) completed a survey containing MM-CGI / SF and other associated psychological measures. Exploratory factor analysis was employed to compare eight alternative factor analytical models to determine the optimal model. Internal-consistency reliability was assessed by Cronbach’s α and construct validity was assessed by Spearman’s correlation-coefficient.
Results: The best fitting model was the MM-CGI-SF three factor model (Personal Sacrifice and Burden, Heartfelt Sadness and Longing and Worry and Felt Isolation). The MM-CGI-SF three factor model demonstrated internal consistency reliability and factor correlations with associated psychological measures indicated construct validity.
Conclusion: The MM-CGI-SF three factor model demonstrated adequate fit and utility, however, the Worry and Felt Isolation subscale needs further replication and revision to assess its dimensionality. The MM-CGI-SF is the more useful tool due to its brevity and better model fit.
Original language | English |
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Pages (from-to) | 320-327 |
Number of pages | 7 |
Journal | Aging and Mental Health |
Volume | 26 |
Issue number | 2 |
Early online date | 5 Nov 2020 |
DOIs | |
Publication status | Published online - 5 Nov 2020 |
Bibliographical note
Funding Information:While grief is a part of common language and universal in nature it is a relatively novel construct in dementia caregiving literature despite its undoubted relevance. Approximately 700,000 family and friends are dementia carers in the UK (Alzheimer’s Research UK, ). These family members provide a wide range of care and support ranging from the most basic of assistance to full responsibility for all personal needs. They are fundamental to the quality of life of care-recipients and essential to the fabric of our society in an aging population. Their value is supported by the NICE-recommended dementia care pathway, which endorses offering caregiver assessments and intervention/s for their emotional, psychological and social needs (National Collaborating Centre for Mental Health, ; NICE guideline, ).
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Keywords
- Anticipatory grief
- burden
- contemporary analytic techniques
- family members
- Gerontology
- Psychiatry and Mental health
- Geriatrics and Gerontology
- Phychiatric Mental Health