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本文引用的文献

1
2016 Alzheimer's disease facts and figures.2016 年阿尔茨海默病事实和数据。
Alzheimers Dement. 2016 Apr;12(4):459-509. doi: 10.1016/j.jalz.2016.03.001.
2
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PLoS One. 2016 Mar 16;11(3):e0150881. doi: 10.1371/journal.pone.0150881. eCollection 2016.
3
The future of mental health care: peer-to-peer support and social media.心理健康照护的未来:朋辈支持和社交媒体。
Epidemiol Psychiatr Sci. 2016 Apr;25(2):113-22. doi: 10.1017/S2045796015001067. Epub 2016 Jan 8.
4
Alzheimer disease. Donepezil and nursing home placement--benefits and costs.阿尔茨海默病。多奈哌齐与疗养院安置——获益与成本。
Nat Rev Neurol. 2016 Jan;12(1):11-3. doi: 10.1038/nrneurol.2015.237.
5
SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks.SentiHealth-癌症:一种用于帮助检测在线社交网络中患者情绪的情感分析工具。
Int J Med Inform. 2016 Jan;85(1):80-95. doi: 10.1016/j.ijmedinf.2015.09.007. Epub 2015 Oct 16.
6
Can clinical use of Social Media improve quality of care in mental Health? A Health Technology Assessment approach in an Italian mental health service.社交媒体的临床应用能否改善心理健康护理质量?意大利心理健康服务中的一项卫生技术评估方法。
Psychiatr Danub. 2015 Sep;27 Suppl 1:S103-10.
7
A content analysis of depression-related Tweets.与抑郁症相关推文的内容分析。
Comput Human Behav. 2016 Jan 1;54:351-357. doi: 10.1016/j.chb.2015.08.023.
8
Blogging and Social Media for Mental Health Education and Advocacy: a Review for Psychiatrists.博客和社交媒体在心理健康教育和倡导中的应用:精神科医生的综述。
Curr Psychiatry Rep. 2015 Nov;17(11):88. doi: 10.1007/s11920-015-0629-2.
9
Sharing feelings online: studying emotional well-being via automated text analysis of Facebook posts.在线分享情感:通过对脸书帖子的自动文本分析研究情绪健康状况。
Front Psychol. 2015 Jul 23;6:1045. doi: 10.3389/fpsyg.2015.01045. eCollection 2015.
10
Characterizing Sleep Issues Using Twitter.利用推特描述睡眠问题
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在世界阿尔茨海默病日提及痴呆症相关内容的推文能让我们了解到哪些心理健康需求?

What Can We Learn About Mental Health Needs From Tweets Mentioning Dementia on World Alzheimer's Day?

作者信息

Yoon Sunmoo

机构信息

Sunmoo Yoon, PhD, RN, Columbia University, New York, NY, USA

出版信息

J Am Psychiatr Nurses Assoc. 2016 Nov;22(6):498-503. doi: 10.1177/1078390316663690.

DOI:10.1177/1078390316663690
PMID:27803262
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5337405/
Abstract

BACKGROUND

Twitter can address the mental health challenges of dementia care. The aims of this study is to explore the contents and user interactions of tweets mentioning dementia to gain insights for dementia care.

METHODS

We collected 35,260 tweets mentioning Alzheimer’s or dementia on World Alzheimer’s Day, September 21 in 2015. Topic modeling and social network analysis were applied to uncover content and structure of user communication.

RESULTS

Global users generated keywords related to mental health and care including #psychology and #mental health. There were similarities and differences between the UK and the US in tweet content. The macro-level analysis uncovered substantial public interest on dementia. The meso-level network analysis revealed that top leaders of communities were spiritual organizations and traditional media.

CONCLUSIONS

The application of topic modeling and multi-level network analysis while incorporating visualization techniques can promote a global level understanding regarding public attention, interests, and insights regarding dementia care and mental health.

摘要

背景

推特可应对痴呆症护理中的心理健康挑战。本研究旨在探究提及痴呆症的推文内容及用户互动情况,以获取痴呆症护理方面的见解。

方法

我们收集了2015年9月21日世界阿尔茨海默病日提及阿尔茨海默病或痴呆症的35260条推文。应用主题建模和社会网络分析来揭示用户交流的内容和结构。

结果

全球用户生成了与心理健康及护理相关的关键词,包括#心理学和#心理健康。英国和美国的推文内容既有相似之处,也有不同之处。宏观层面分析发现公众对痴呆症关注度颇高。中观层面网络分析显示,社区的顶级领导者是精神组织和传统媒体。

结论

运用主题建模和多层次网络分析并结合可视化技术,有助于在全球层面增进对公众对痴呆症护理及心理健康的关注、兴趣和见解的理解。