The relationship between trait emotional intelligence, resiliency, and mental health in older adults: the mediating role of savouring
Bibliographic record
Abstract
OBJECTIVES: The present study explores savouring, defined as the process of attending to positive experiences, as a mediator in the relationships between resiliency, trait emotional intelligence (EI), and subjective mental health in older adults. Following Fredrickson's Broaden and Build Theory of positive emotions, the present study aims to extend our understanding of the underlying processes that link resiliency and trait EI with self-reported mental health in older adulthood. METHOD: A sample of 149 adults aged 65 and over (M = 73.72) were recruited from retirement homes and community groups. Participants completed measures of resiliency, savouring, trait EI, and subjective mental health either online or in a paper format. RESULTS: Path analysis revealed that savouring fully mediated the relationship between resiliency and mental health. However, trait EI did not significantly predict mental health in this sample. CONCLUSION: These findings provided partial support for the Broaden and Build Theory of positive emotions. As anticipated, savouring imitated the broadening effect of positive emotions by mediating the relationship between resiliency and mental health. However, savouring failed to reflect the undoing effect of positive emotions and did not mediate the relationship between EI and mental health. These findings have implications for positive psychology exercises and may be a simple, yet effective means of improving the life quality of older adults.
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 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".