Stress and Adjustment During the COVID-19 Pandemic: A Qualitative Study on the Lived Experience of Canadian Older Adults
Bibliographic record
Abstract
In response to the COVID-19 pandemic, social distancing measures were put into place to flatten the pandemic curve. It was projected that older adults were at increased risk for poor psychological and health outcomes resulting from increased social isolation and loneliness. However, little research has supported this projection among community-dwelling older adults. While a growing body of research has examined the impact of the COVID-19 pandemic on older adults, there is a paucity of qualitative research that captures the lived experience of community-dwelling older adults in Canada. The current study aimed to better understand the lived experience of community-dwelling older adults during the first six months of the pandemic in Ontario, Canada. Semi-structured one-on-one interviews were conducted with independent-living older adults aged 65 years and older. A total of 22 interviews were analyzed using inductive thematic analysis. Following a recursive process, two overarching themes were identified: perceived threat and challenges of the pandemic, and coping with the pandemic. Specifically, participants reflected on the threat of contracting the virus and challenges associated with living arrangements, social isolation, and financial insecurity. Participants shared their coping strategies to maintain health and wellbeing, including behavioral strategies, emotion-focused strategies, and social support. Overall, this research highlights resilience among older adults during the first six months of the 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".