Evidence map›Paper›PMID 36002624›Full record

ArticleBehavior research methods2023

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

Aki-Juhani Kyröläinen, James Gillett, Megan Karabin, Ranil Sonnadara, Victor Kuperman

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Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Aki-Juhani KyröläinenDepartment of Linguistics and Languages, McMaster University, Togo Salmon Hall 513, 1280 Main Street West, Hamilton, Ontario, Canada, 8S 4M2. akkyro@gmail.com.
James GillettMcMaster University, Hamilton, Canada.
Megan KarabinMcMaster University, Hamilton, Canada.
Ranil SonnadaraMcMaster University, Hamilton, Canada.
Victor KupermanMcMaster University, Hamilton, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Memory, EpisodicAgedAgingCognitionHumansMental RecallMiddle AgedPandemicsAgingAutobiographical memoryCOVID-19Text analytics

Identifiers

PMID36002624
PMCPMC9400578

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.