Resilience among older adults during the COVID-19 pandemic: A photovoice study
Bibliographic record
Abstract
Older adults faced significant challenges during the COVID-19 pandemic but also demonstrated great resilience. Investigating these strengths may enhance and inform strategies to mitigate the impacts of the pandemic. To gain insight into the resilience processes of older adults during the first year of the pandemic, we conducted a photovoice study with 26 older adults (aged over 60) in the province of Quebec, Canada. Participants met online weekly for three weeks in small groups to discuss their photographs and share their resilience strategies. The thematic analysis revealed three interrelated themes. First, participants distanced themselves from the pandemic by engaging in activities that took their focus away from COVID-19 and that afforded much-needed respite. Second, participants regained their bearings by reorganizing their schedules and establishing new routines that bolstered occupation rather than rumination. Third, participants used the pandemic to self-reflect and revise their priorities, leveraging the pandemic as an opportunity for growth. Together, these themes demonstrate the strengths, coping strategies and resilience of older adults and contrast the stereotypes of older adults as vulnerable and resourceless. These findings have the potential to inform the implementation of strength-based health promotion initiatives to mitigate the harms 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".