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Identification of stroke carers 'at risk': a preliminary study of the predictors of carers' psychological well-being at one year post stroke
Background: A third of carers of people who have had a stroke experience poor psychological health. Early prediction of carers' later well-being would allow interventions to be targeted at carers most at risk of a poor outcome. Aim: To develop a predictive model for the identification of stroke...
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Published in: | Clinical rehabilitation 1996-11, Vol.10 (4), p.337-346 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Background: A third of carers of people who have had a stroke experience poor psychological health. Early prediction of carers' later well-being would allow interventions to be targeted at carers most at risk of a poor outcome.
Aim: To develop a predictive model for the identification of stroke carers at risk of developing poor psychological well-being in the first year post stroke.
Method: Fifty consecutive one-year survivors of a stroke and their carers were assessed on a range of demographic, environmental and psychosocial variables at three months post stroke. The carers' psychological well-being at 12 months post stroke was assessed using the General Well-Being Index (GWBI). Multiple regression analysis was performed to identify the best early predictors of carers' later well-being.
Results: The regression analysis identified three significant predictors of carers' well-being: carers' appraisal of caregiving stress; carers' physical health; and carers' satisfaction with service provision (r
2 = 0.53). Using this model, 72% of carers' predicted well-being scores fell within nine points of their actual 12-month GWBI score.
Conclusion: Findings from this preliminary study indicate that it may be possible to identify carers most likely to experience poor psychological well- being. In order to develop a clinically usable 'carer risk prediction instrument' further research to test this model is essential. |
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ISSN: | 0269-2155 1477-0873 |
DOI: | 10.1177/026921559601000413 |