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老年人的认知和社会福利:书面生活故事的 CoSoWELL 语料库。

Cognitive and social well-being in older adulthood: The CoSoWELL corpus of written life stories.

机构信息

Department of Linguistics and Languages, McMaster University, Togo Salmon Hall 513, 1280 Main Street West, Hamilton, Ontario, Canada, 8S 4M2.

McMaster University, Hamilton, Canada.

出版信息

Behav Res Methods. 2023 Sep;55(6):2885-2909. doi: 10.3758/s13428-022-01926-0. Epub 2022 Aug 24.

Abstract

This paper presents the Cognitive and Social WELL-being (CoSoWELL) project that consists of two components. One is a large corpus of narratives written by over 1000 North American older adults (55+ years old) in five test sessions before and during the first year of the COVID-19 pandemic. The other component is a rich collection of socio-demographic data collected through a survey from the same participants. This paper introduces the first release of the corpus consisting of 1.3 million tokens and the survey data (CoSoWELL version 1.0). It also presents a series of analyses validating design decisions for creating the corpus of narratives written about personal life events that took place in the distant past, recent past (yesterday) and future, along with control narratives. We report results of computational topic modeling and linguistic analyses of the narratives in the corpus, which track the time-locked impact of the COVID-19 pandemic on the content of autobiographical memories before and during the COVID-19 pandemic. The main findings demonstrate a high validity of our analytical approach to unique narrative data and point to both the locus of topical shifts (narratives about recent past and future) and their detailed timeline. We make the CoSoWELL corpus and survey data available to researchers and discuss implications of our findings in the framework of research on aging and autobiographical memories under stress.

摘要

本文介绍了认知和社会幸福感(CoSoWELL)项目,该项目由两个部分组成。一个是由 1000 多名北美老年人(55 岁以上)在 COVID-19 大流行前和第一年的五次测试中撰写的大量叙事语料库。另一个组成部分是通过同一参与者的调查收集的丰富的社会人口统计数据。本文介绍了语料库的第一个版本,该版本包含 130 万个标记和调查数据(CoSoWELL 版本 1.0)。它还介绍了一系列分析,这些分析验证了创建关于过去、最近(昨天)和未来个人生活事件的叙事的语料库的设计决策,以及控制叙事。我们报告了语料库中叙事的计算主题建模和语言分析的结果,这些结果追踪了 COVID-19 大流行对 COVID-19 大流行前和大流行期间自传记忆内容的时间锁定影响。主要发现证明了我们对独特叙事数据的分析方法具有很高的有效性,并指出了主题转变的位置(关于最近过去和未来的叙事)及其详细的时间表。我们向研究人员提供了 CoSoWELL 语料库和调查数据,并在压力下研究衰老和自传记忆的框架内讨论了我们发现的意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0ca/10556138/7f26fab637f7/13428_2022_1926_Fig1_HTML.jpg

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