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Record W7117155781 · doi:10.1177/26335565251408339

The experience of older adults living with multimorbidity during the COVID-19 pandemic: A phenomenological study

2025· article· en· W7117155781 on OpenAlexafffund
B. Ryan, Judith Belle Brown, Saifora Paktiss, Amanda Terry

Bibliographic record

VenueJournal of Multimorbidity and Comorbidity · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsSocial isolationCoping (psychology)Thematic analysisInterpretative phenomenological analysisPandemicPsychological resilienceSocial supportPsychological interventionIsolation (microbiology)

Abstract

fetched live from OpenAlex

Purposes: We explored the experience of older adults living with multimorbidity during the COVID-19 pandemic. Methods: Using a phenomenological approach, we conducted semi-structured interviews with 17 participants aged 50 years and older living with multimorbidity. Data collection and analysis were iterative using a thematic approach. We explored participants’ overall pandemic experience as well as querying specifically about pandemic experiences of social isolation, coping and resilience. Results: We describe our findings according to four main themes: (1) Lives disrupted; (2) Diverse social isolation experiences; (3) Coping through seeking solitary and group activities; and (4) Individual ways of enacting resilience and the role of health care to build resilience. Conclusion: We found the experiences of older adults living with multimorbidity during the COVID-19 pandemic were characterized by disruption. When queried social isolation was shared as a prominent concern. We identified the creativity with which participants coped with social isolation and the resilience they marshaled. This study will be used to inform interventions to mitigate social isolation and its effects in a post-pandemic world.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.356
Teacher spread0.295 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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