Exploring older adults’ lived experiences of COVID-19: A narrative inquiry study
Bibliographic record
Abstract
Abstract COVID-19 dramatically changed daily life for older adults in numerous and complex ways. Research is calling for an understanding on how COVID-19 has and will impact aging, and older adults’ lived experiences with aging, within the context of the pandemic. Social and physical distancing guidelines have put older adults at an increased risk for social isolation. Intergenerational tensions have also intensified during the pandemic, and early research states the labeling of older adults as a homogenous and “vulnerable” group can lead to an increased risk of ageism in their communities. Therefore, the purpose of this study is to explore how community-dwelling older adults (ages 65 and greater) experience daily life amid the COVID-19 pandemic using a biopsychosocial approach. This study employs a critical qualitative narrative inquiry design. Data will be collected through solicited diaries and semi-structured individual interviews (via telephone and video conferencing software). Data will be analyzed thematically and involve a re-storying of the findings. Preliminary results will be presented and discussed. This study aims to inform new and critical perspectives that broaden our understanding of how the overall health, wellness, and quality of life of older adults can be supported. Findings contribute to the current and developing knowledge of older adults’ first-person accounts of their experiences within the COVID-19 pandemic.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".