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Effects of Written Emotional Disclosure on Burden, Trauma Symptoms and Health Status in Primary Caregivers of The Patient: A meta-analysis

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摘要 Objective: The meta-analysis aims to evaluate the efficacy of written emotional disclosure on burden, trauma symptoms and health status in primary caregivers of the patient. Methods: Randomized controlled trials (RCTs) of written emotional disclosure in primary caregivers of the patient were retrieved from multiple electronic databases such as the Cochrane Library, PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure (CNKI), VIP Database for Chinese Technical Periodicals (VIP), Chinese Biomedicine Database (CBM) and Wan Fang database (Wan Fang) until August 2019. The eligible studies were screened and evaluated independently by two researchers. Then, we analyzed the extracted data by RevMan 5.3 software. Results: A total of 7 randomized controlled trials (RCT) with 352 primary caregivers were included. The quality evaluation grade of the 3 articles included in the literature was “A”, and the rests was “B”. Meta-analysis showed that compared with the control group, written emotional disclosure could alleviate the burden of patients' primary caregivers [SMD = 0.26, 95% CI (0.02, 0.49), P = 0.03]. The results showed that there was no significant difference between the two groups in alleviating trauma-like symptoms [MD = 3.30, 95% CI (-4.52, 11.11), P = 0.41]. There was no significant difference in the mean score of the General Health Questionnaire (GHQ) between the two groups [MD =-1.94, 95% CI (-11.11, 7.23), P = 0.68]. Besides, there was no significant difference between the two groups in improving their physical and mental health [SMD = 0.28, 95% CI (-0.17, 0.73), P = 0.22;SMD =-0.43, 95% CI (-0.88, 0.02), P = 0.06]. Conclusion: Written emotional disclosure can effectively alleviate the burden in patients' primary caregivers. However, the effects on trauma-like symptoms, the GHQ score and health status need to be verified by further studies.
出处 《Medical Data Mining》 2019年第3期126-133,共8页 TMR医学数据挖掘
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