Negative emotions associated with self-growth among older adults during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction The Covid-19 pandemic appeared globally (1), thus affecting the self-growth of the older population (2). Objectives The aim of this study is to identify and analyze the negative emotions felt during the pandemic, as well as their impact on self-growth of 226 older individuals of four nationalities: Mexican, Italian, Portuguese and Spanish. Methods Thus, a transnational qualitative survey was carried out. A content analysis was performed. Results Seven negative emotions were reported, namely: fear, sadness, anger, grief, annoyance, loneliness and shame. These emotions were considerably associated with the following themes: (1) Sharing experiences; (2) Availability of the partner; (3) Spirituality and religion; (4) Be active; (5) Interest in new projects; (6) Civic participation; (7) Sexual activity. Older participants with Mexican and Italian nationality reported that sharing experiences as the most relevant topic, while for the Portuguese and Spanish participants, having a partner available was more important. Conclusions This study demonstrated that negative emotions cooperated with the self-growth of older individuals during the Covid-19 pandemic. The heterogeneity of experiences lived by each culture was highlighted, underlining the positive side of negative emotions and their strong connection with the self-growth of the older people. 1.von Humboldt S et al. Smart technology and the meaning in life of older adults during the Covid-19 public health emergency period: A cross-cultural qualitative study. Int Rev Psychiatry, 2020; 1-10. 2. von Humboldt S et al. Does spirituality really matter? - A study on the potential of spirituality to older adult’s adjustment to aging. Jpn Psychol Res, 56;114-125. Disclosure No significant relationships.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".