COVID-19: The Untold Story - A Qualitative Exploration of the Impacts of the Pandemic on Older Adults with Multimorbidity
Bibliographic record
Abstract
There are direct impacts of having COVID-19 infection as well as wide reaching indirect impacts for the general population who experienced the pandemic, such as social isolation. Older adults and those with multimorbidity were among the population most impacted. Thus this research explored the direct and indirect impacts of the COVID-19 pandemic on older adults with multimorbidity. Objective: To gain an in-depth understanding of the direct and indirect impacts of the pandemic on older adults with multimorbidity by exploring their lived experiences. Study Design and Analysis: A phenomenological approach was utilized. Seventeen in-depth interviews were conducted with older adults who had multimorbidity. Team members included researchers and a patient partner. Thematic analysis was conducted. Setting: Province of Ontario, Canada. Population studied: Adults 50+ years of age who had two or more chronic conditions. Results: Overall, participants described varied impacts and experiences that were linked with their own contexts and unique situations. Four main themes emerged from the data analysis. Experiences Are Individual: Participants had different overall experiences of living through the pandemic – ranging from “not so bad” to “truly awful”, some having contracted COVID-19. Social Isolation: Social isolation was a profound and defining pandemic experience for many participants. For others, it was not as great a challenge. Lives Disrupted: Disruptions occurred in participants’ lives; the degree of disruption ranged from inconvenient (difficulty getting groceries) to the dire (unable to be with family member at death). How to Cope: While some participants coped with the pandemic by taking it day by day, most described specific activities they used to cope including activities that were solitary (e.g., yoga, reading) and those that were interactive or social (e.g., virtual connections with family, friends, groups). Conclusion: This study revealed the impacts of the COVID-19 pandemic were highly variable depending on participants’ personal situations, with main impacts reported being social isolation and disruption in their lives. Many used coping strategies and, as with impacts, the nature, extent and usefulness of these strategies varied among participants. Our findings highlight the importance of understanding patients’ context when working with them to address on-going impacts such as social isolation and loneliness.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".