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Record W4410577873 · doi:10.1177/08982643251345422

The Impact of Ageism and Pain on Pandemic-Related Stress in Older Adults: A Structural Equation Modeling and Mediation Analysis

2025· article· en· W4410577873 on OpenAlexafffund
Kylie A. Arsenault, Ying C. MacNab, Gordon J. G. Asmundson, Thomas Hadjistavropoulos

Bibliographic record

VenueJournal of Aging and Health · 2025
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of British ColumbiaUniversity of Regina
FundersSaskatchewan Health Research Foundation
KeywordsMediationPandemicStructural equation modelingContext (archaeology)PsychologyPsychological interventionPublic healthStress (linguistics)Clinical psychologyGerontologyMedicineCoronavirus disease 2019 (COVID-19)PsychiatrySociologyDiseaseNursing

Abstract

fetched live from OpenAlex

Although research has linked ageism and pain to increased stress in older adults, their influence on stress within the context of pandemics has not been adequately examined. Our objective was to investigate relationships among pain, ageism, and pandemic-related stress in older adults using structural equation modeling (SEM) and mediation analysis. We hypothesized that pain would exert a direct and/or indirect influence on pandemic-related stress, and ageism a direct influence. Data were collected from 486 North American older adults in January 2024. Participants completed measures of pain, ageism, and pandemic-related stress. SEM and mediation analyses yielded evidence suggesting ageism and pain influence pandemic-related stress, although ageism mediated pain's impact on pandemic-related stress. This study underscores how pain and ageism can impact older adults' psychological well-being during pandemics. Findings highlight a potential need for public health interventions to address ageism and pain during future waves of COVID-19 or other global health 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)0.000
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 score0.241
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.438
Teacher spread0.396 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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