The Impact of Ageism and Pain on Pandemic-Related Stress in Older Adults: A Structural Equation Modeling and Mediation Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".