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Research Of Protective Factors Of Resilience In Elderly Patients With Chronic Diseases

Posted on:2020-11-15Degree:MasterType:Thesis
Country:ChinaCandidate:M M LouFull Text:PDF
GTID:2404330590484942Subject:Mental Health
Abstract/Summary:PDF Full Text Request
Objectives The purpose of this research was to understand the characteristics of resilience in elderly patients with chronic diseases,explore the influencing factors of high and low resilience in elderly patients with chronic diseases,explore the protective factors of extracting resilience in elderly patients with chronic diseases,and construct a model of protective factors of resilience in elderly patients with chronic diseases.Methods Quantitative study using self-designed demographic variable questionnaire,Connor-Davidson Resilience Scale(CD-RISC),simplified coping style questionnaire(SCSQ),family care index questionnaire(APGRA),understanding social support scale(PSSS),Pittsburgh sleep index scale(PSQI),the overall well-being scale(GWB),the chronic disease self-management inventory(CDSMS)a questionnaire survey was conducted among 1166 elderly patients with chronic diseases in Tangshan,Hebei province.SPSS19.0 statistical software was used for analysis.The qualitative study used the phenomenological research method to conduct in-depth interviews with 20 elderly patients with chronic diseases.The interview data were sorted out and analyzed with the help of Nvivo8.0 software.Results Quantitative study: 1 The total score of resilience of elderly patients with chronic diseases was(58.82 +13.50),which was lower than the domestic norm.2 There were significant differences in residence,education,income,diet and medical treatment(P<0.05).3 There were significant differences in the total scores of SCSQ,PSSS,GWB,PSQI,CDSMS,APGRA and PSQI between high and low resilience patients(P<0.05).4 The total score of resilience was positively correlated with SCSQ,APGRA,PSSS,PSSS,GWB,and the total score of chronic disease self-management ability(P<0.01),negatively correlated with the total score of PSQI(P<0.01).The results of stepwise multiple regression analysis showed that cognitive ability,psychological status,behavioral lifestyle and social environment can predict the resilience level of elderly patients with chronic diseases in positive coping style,negative coping style,sleep quality,satisfaction and interest in life,health worries,depression or happy mood,other support and self-management of chronic diseases.Qualitative study: 1 Through indepth interviews on the real experience of elderly patients with chronic diseases,5 topics were refined: emotional control;coping ability;social support;cognitive level;andintervention management.2 The protective factors of elderly patients with chronic diseases are positive and optimistic attitude,effective coping style,external support(family,friends,medical treatment)and correct cognitive management.Risk factors include negative emotions,negative coping style and low level cognitive management.Conclusion 1 Psychological resilience level of elderly patients with chronic diseases is generally low;negative coping style,low sleep quality and family history can reduce the level of psychological resilience of elderly patients with chronic diseases.2 Living in the city,the higher level of education,no family history,balanced diet,with staff health insurance and the higher economic income level are good for psychological resilience of elderly patients with chronic disease.3 The higher level of the positive coping style,family care,social support,sleep quality,general well-being and self-management ability of chronic diseases,the better psychological resilience level.4 Optimistic and positive mentality,family attention,cognitive level and intervention management in qualitative research are protective factors of resilience in elderly patients with chronic diseases.Risk factors are disease-related symptoms,uncertainty of rehabilitation and worrying about the future.Figure 2;Table 21;Reference 131...
Keywords/Search Tags:elderly patients with chronic diseases, resilience, protective factors model
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