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Record W4281796323 · doi:10.1177/07334648221105062

‘Make the Most of the Situation’. Older Adults’ Experiences during COVID-19: A Longitudinal, Qualitative Study

2022· article· en· W4281796323 on OpenAlexaff
Emily Brooks, Somayyeh Mohammadi, W. Ben Mortenson, Catherine L. Backman, Chihori Tsukura, Isabelle Rash, Janice Chan, William C. Miller

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsInternational Collaboration On Repair DiscoveriesGF Strong Rehabilitation CentreUniversity of British Columbia
Fundersnot available
KeywordsPandemicContentmentCoronavirus disease 2019 (COVID-19)Social isolationQualitative researchGerontologyPsychologyPleasureLongitudinal studyAging in placeQuality of life (healthcare)Older peopleDevelopmental psychologySocial psychologySociologyMedicineDiseasePsychiatry

Abstract

fetched live from OpenAlex

The COVID-19 pandemic restrictions have been associated with increased social isolation and reduced participation in older adults. This longitudinal qualitative study drew on life course theory to analyse data from a series of four sequential semi-structured interviews conducted between May 2020–February 2021 with adults aged 65+ ( n = 12) to explore older adults’ experiences adjusting to the COVID-19 pandemic. We identified three themes: (1) Struggling ‘You realize how much you lost’ describes how older adults lost freedoms, social connections and activities; (2) Adapting ‘whatever happens, happens, I’ll do my best’, revealing how older adults tried to maintain well-being, participation and connection; and (3) Appreciating ‘enjoy what you have’, exploring how older adults found pleasure and contentment. Engagement in meaningful activities and high-quality social interactions supported well-being during the COVID-19 pandemic for older adults. This finding highlights the need for policies and services to promote engagement during longstanding global crises.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.410
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2022
Admission routes1
Has abstractyes

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