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临终生活:反映在癌症患者推文里的临终轨迹。

#Deathbedlive: the end-of-life trajectory, reflected in a cancer patient's tweets.

机构信息

eHealth Research Group, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.

Ernst and Young AG, Basel, Switzerland.

出版信息

BMC Palliat Care. 2018 Jan 22;17(1):17. doi: 10.1186/s12904-018-0273-9.

DOI:10.1186/s12904-018-0273-9
PMID:29357865
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5778813/
Abstract

BACKGROUND

Understanding physical and psycho-social illness trajectories towards the end of life can help in the planning of palliative and supportive care. With terminal patients increasingly seeking and sharing health information and support via social media, it is timely to examine whether these trajectories are reflected in their digital narratives. In this exploratory study, we analysed the Twitter feed of prominent cancer sufferer and physician, Kate Granger, over the final 6 months of her life.

METHODS

With the consent of Kate's widower, Chris Pointon, 1628 Twitter posts from @GrangerKate were manually screened. The 550 tweets judged relevant to her disease were qualitatively content analysed with reference to the six modifiable dimensions of the patient experience in Emanuel and Emanuel's 'framework for a good death'. The frequency of each tweet category was charted over time and textual content was examined and cross-referenced with key events, to obtain a deeper understanding of its nature and significance.

RESULTS

Tweets were associated with physical symptoms (N = 270), psychological and cognitive symptoms (N = 213), social relationships and support (N = 85), economic demands and care giving needs (N = 85), hopes and expectations (N = 51) and spiritual beliefs (N = 7). While medical treatments and procedures were discussed in detail, medical information-seeking was largely absent, likely reflecting Kate clinical expertise. Spirituality was expressed more as hope in treatments or "someone out there listening", than in religious terms. The high value of Kate's palliative care team was a dominant theme in the support category, alongside the support she received from her online community of fellow sufferers, friends, family and colleagues. Significant events, such as medical procedures and hospital stays generated the densest Twitter engagement. Transitions between trajectory phases were marked by changes in the relative frequency of tweet-types.

CONCLUSIONS

In Kate's words, "the power of patient narrative cannot be underestimated". While this analysis spanned only 6 months, it yielded rich insights. The results reflect theorised end-of-life dimensions and reveal the potential of social media data and digital bio-ethnography to shine a light on terminal patients' lived experiences, coping strategies and support needs, suggesting new opportunities for enhancing personalised palliative care and avenues for further research.

摘要

背景

了解生命末期的身体和心理社会疾病轨迹有助于规划姑息治疗和支持性护理。随着终末期患者越来越多地通过社交媒体寻求和分享健康信息和支持,现在及时检查这些轨迹是否反映在他们的数字叙述中是很重要的。在这项探索性研究中,我们分析了著名癌症患者和医生凯特·格兰杰(Kate Granger)在生命的最后 6 个月的推特(Twitter)feed。

方法

在凯特的遗孀克里斯·波顿(Chris Pointon)的同意下,手动筛选了来自@GrangerKate 的 1628 条推文。与她的疾病相关的 550 条推文与 Emanuel 和 Emanuel 的“美好死亡框架”中的六个可改变的患者体验维度进行了定性内容分析。每个推文类别的频率随时间绘制,并对文本内容进行检查和交叉引用关键事件,以更深入地了解其性质和意义。

结果

推文与身体症状(N=270)、心理和认知症状(N=213)、社会关系和支持(N=85)、经济需求和护理需求(N=85)、希望和期望(N=51)和精神信仰(N=7)相关。虽然详细讨论了医疗治疗和程序,但主要缺乏医疗信息搜索,这可能反映了凯特的临床专业知识。灵性更多地表达为对治疗的希望,或者“有人在听”,而不是宗教术语。凯特姑息治疗团队的高度重视是支持类别中的一个主要主题,此外还有她从在线患者社区、朋友、家人和同事那里获得的支持。重大事件,如医疗程序和住院治疗,产生了最密集的推特参与度。轨迹阶段之间的转变以推文类型的相对频率的变化为标志。

结论

用凯特的话说,“患者叙述的力量不可低估”。虽然这一分析仅跨越了 6 个月,但却产生了丰富的见解。结果反映了临终维度的理论化,并揭示了社交媒体数据和数字生物伦理学的潜力,可以揭示终末期患者的生活体验、应对策略和支持需求,为增强个性化姑息治疗提供新的机会,并为进一步研究开辟新途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5b4/5778813/e58ea8c1bd0c/12904_2018_273_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5b4/5778813/865b99b23187/12904_2018_273_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5b4/5778813/409131b65ba3/12904_2018_273_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5b4/5778813/e58ea8c1bd0c/12904_2018_273_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5b4/5778813/865b99b23187/12904_2018_273_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5b4/5778813/409131b65ba3/12904_2018_273_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5b4/5778813/e58ea8c1bd0c/12904_2018_273_Fig3_HTML.jpg

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