Insights into the impact on daily life of the COVID-19 pandemic and effective coping strategies from free-text analysis of people's collective experiences.
Insights into the impact on daily life of the COVID-19 pandemic and effective coping strategies from free-text analysis of people's collective experiences.
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DOI：
10.1098/rsfs.2021.0051
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发表时间：
2021-12-06
期刊：
影响因子：
4.4
通讯作者：
Chamberlain SR
中科院分区：
文献类型：
--
作者：
Hampshire A;Hellyer PJ;Trender W;Chamberlain SR
关于人们在COVID-19大流行期间如何科普的猜测很多;然而，需要从预先指定的答案中选择的调查受到研究人员观点的限制，可能会忽略最有效的措施。在这里，我们采用了一种无偏见的方法，通过应用自然语言处理他们的自由文本报告，从人们的集体生活经验中学习。在英国首次封城高峰期，有51 113人就疫情的正面和负面影响，以及他们认为在此期间有帮助的实际措施，提供了自由文本回应。潜在狄利克雷分配以不受约束的数据驱动方式确定了最常见的影响和建议主题。我们报告说，六个消极主题和七个积极主题是捕捉人们报告受到大流行影响的不同方式的最佳选择。需要45个专题来最佳地总结他们建议的实际应对战略。一般线性模型显示，这些主题的流行程度随年龄的变化而变化。我们建议，丰富的应对措施可以从普通民众的生活经验中提炼出来。这些可以为在大流行期间和之后具有相关性的可行的个性化数字干预提供信息。
There has been considerable speculation regarding how people cope during the COVID-19 pandemic; however, surveys requiring selection from prespecified answers are limited by researcher views and may overlook the most effective measures. Here, we apply an unbiased approach that learns from people's collective lived experiences through the application of natural-language processing of their free-text reports. At the peak of the first lockdown in the United Kingdom, 51 113 individuals provided free-text responses regarding self-perceived positive and negative impact of the pandemic, as well as the practical measures they had found helpful during this period. Latent Dirichlet Allocation identified, in an unconstrained data-driven manner, the most common impact and advice topics. We report that six negative topics and seven positive topics are optimal for capturing the different ways people reported being affected by the pandemic. Forty-five topics were required to optimally summarize the practical coping strategies that they recommended. General linear modelling showed that the prevalence of these topics covaried substantially with age. We propose that a wealth of coping measures may be distilled from the lived experiences of the general population. These may inform feasible individually tailored digital interventions that have relevance during and beyond the pandemic.
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影响因子：
15.1
作者：
Hampshire A;Trender W;Chamberlain SR;Jolly AE;Grant JE;Patrick F;Mazibuko N;Williams SC;Barnby JM;Hellyer P;Mehta MA
通讯作者：
Mehta MA
影响因子：
2.6
作者：
Commodari, Elena;La Rosa, Valentina Lucia;Parisi, Jessica
通讯作者：
Parisi, Jessica
影响因子：
6
作者：
Blei, DM;Ng, AY;Jordan, MI
通讯作者：
Jordan, MI
DOI：
10.3390/ijerph17165728
发表时间：
2020-08-01
影响因子：
--
作者：
Ding, Yubin;Xu, Junling;Xie, Shenghua
通讯作者：
Xie, Shenghua
影响因子：
6.3
作者：
Niedzwiedz CL;Green MJ;Benzeval M;Campbell D;Craig P;Demou E;Leyland A;Pearce A;Thomson R;Whitley E;Katikireddi SV
通讯作者：
Katikireddi SV