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Objective To explore the mediating,direct and indirect roles of self-efficacy,social support and medical coping modes with resilience in patients with chronic hepatitis B.Methods Totally 384 inpatients and outpatients with chronic hepatitis B,without physical ailments or mental problems were selected.The Connor-Davidson resilience scale (CD-RISC),general self-efficacy scale (GSES),medical coping modes questionnaire (MCMQ) and social support rating scale (SSRS) were adopted in this research.The relationship among all variables was explored by path analysis.Results (1)The total scores of resilience,self-efficacy,social support,confrontation,avoidance and acceptance-resignation in chronic hepatitis B patients were (61.64± 15.36),(25.40±5.70),(39.82±7.39),(19.02±3.72),(16.63 ±2.69) and (8.45 ±2.94),respectively.(2)Pearson analysis showed that the scores of self-efficacy,medical coping and social support were significantly correlated with resilience (r=-0.26-0.82,P＜0.05).(3) Importantly,path analysis showed that self-efficacy(β=0.53),confrontation (β=0.10),avoidance (β3=0.17) and acceptance-resignation (β=-0.13) had direct prediction effects on resilience.Additionally,self-efficacy was indirectly related to resilience(β3=0.05),and social support also indirectly related to resilience through self-efficacy,confrontation and acceptance-resignation (β=0.18).Conclusion Self-efficacy,social support and medical coping are the important factors which play a direct or indirect role in resilience.
ABSTRACT
Objective@#To explore the mediating, direct and indirect roles of self-efficacy, social support and medical coping modes with resilience in patients with chronic hepatitis B.@*Methods@#Totally 384 inpatients and outpatients with chronic hepatitis B, without physical ailments or mental problems were selected.The Connor-Davidson resilience scale (CD-RISC), general self-efficacy scale (GSES), medical coping modes questionnaire (MCMQ) and social support rating scale (SSRS) were adopted in this research.The relationship among all variables was explored by path analysis.@*Results@#(1)The total scores of resilience, self-efficacy, social support, confrontation, avoidance and acceptance-resignation in chronic hepatitis B patients were (61.64±15.36), (25.40±5.70), (39.82±7.39), (19.02±3.72), (16.63±2.69) and (8.45±2.94), respectively.(2)Pearson analysis showed that the scores of self-efficacy, medical coping and social support were significantly correlated with resilience(r=-0.26-0.82, P<0.05). (3)Importantly, path analysis showed that self-efficacy(β=0.53), confrontation(β=0.10), avoidance(β=0.17) and acceptance-resignation(β=-0.13) had direct prediction effects on resilience.Additionally, self-efficacy was indirectly related to resilience(β=0.05), and social support also indirectly related to resilience through self-efficacy, confrontation and acceptance-resignation(β=0.18).@*Conclusion@#Self-efficacy, social support and medical coping are the important factors which play a direct or indirect role in resilience.
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Objective To review the contents and effects of behavioral risk factor surveillance system (BRFSS) and analyzed the application of BRFSS,citizens' health condition and its related risk factors in China.Then summarize the related problems in using BRFSS.Methods The literatures about Chinese research of BRFSS were searched from four databases in China,and a qualitative analysis was conducted.Results 18 case control or cohort studies were involved in the final qualitative analysis,the results showed that there was a positive tendency of people' s health related knowledge.However,people' s improvement of health behavior was at a slow speed,and the most popular health related risk factors consisted of smoking,alcohol drinking,lack of physical exercise,unbalanced diets,overweight,etc.Conclusion By using BRFSS,people' s health status and related risk factors could be accessed with flexibility timely and accurately.Thus can make great contributions for accessing,analyzing and tracking health status and health behavior of the population,finding the targeted population and evaluating effectiveness of health intervention,and this will also be helpful for the performance of health promotion programs and formulation of health policy.In addition,new technologies should be added into survey approaches and guarantee of data quality.