The Upside of Negative Emotions: How Do Older Adults From Different Cultures Challenge Their Self-Growth During the COVID-19 Pandemic?
Bibliographic record
Abstract
Background and Objective: The outbreak of Coronavirus Disease 2019 (COVID-19) has raised increased challenges for older adults' personal growth in diverse cultural settings. The aim of this study was to analyze negative emotions and their role on older adults' self-growth in Mexico, Italy, Portugal, and Spain, during the COVID-19 pandemic. For this purpose, a cross-national qualitative research was carried out. Methods: Data were collected from 338 community-dwelling participants aged 65 years and older, using a semi-structured interview protocol. Older adults were asked about negative emotions that significantly contribute to their self-growth during the COVID-19 pandemic. Content analysis was used to identify key themes. Results: Seven main negative emotions (fear, sadness, anger, grief, boredom, loneliness, and shame) significantly contributed to seven themes of self-growth, across the samples: sharing difficult experiences with others, supportive partner, spiritual practices, engagement with life, generativity, volunteering activities, and intimacy and sexual satisfaction. Sharing difficult experiences with others was most pertinent to Mexican (13.9%) and to Italian (3.0%) participants, and a supportive partner to Portuguese (12.1%), and to Spanish participants (6.5%). Conclusion: The findings of this study indicate that negative emotions during the COVID-19 pandemic contributed to their older adults' self-growth. This study highlighted the cultural diversity of experiences during the pandemics and underlined the upside of negative emotions and its relation to older adults' self-growth during this period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".