Character Strengths and Resilience in Older Adults during the COVID-19 Pandemic
Bibliographic record
Abstract
During the pandemic, older adults were perceived as a vulnerable group without considering their various strengths. This study explored the associations between character strengths and resilience, and verified if some of these could predict resilience during the COVID-19 pandemic. A sample of 92 participants (women = 79.1%), ≥ 70 years of age (mean = 75.6 years), completed an online version of the Values in Action Inventory of Strengths - Positively keyed (VIA-IS-P) to assess 24 character strengths (grouped under six virtues) and the Connor and Davidson Resilience Scale. Results showed that 20 of the 24 strengths correlated positively and significantly with resilience. A multiple regression analysis revealed that the virtues of courage and transcendence, as well as attitudes toward aging, uniquely predicted the level of resilience. Interventions should be developed to improve certain strengths (e.g., creativity, zest, hope, humor, and curiosity), while reducing ageism, in order to promote resilience.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".