Life Satisfaction and Self-Esteem in Older Adults Engaging in Formal Volunteering: A Cross-Sectional Study in Taiwan
Bibliographic record
Abstract
Previous research has reported an association between life satisfaction, self-esteem, and volunteering. However, it is unclear whether self-esteem is associated with life satisfaction in older adults who are already engaged in volunteering. Therefore, the present study aimed to investigate the association between life satisfaction and self-esteem in older adults who were formally volunteering at a non-governmental organization in Taiwan. A cross-sectional study was conducted on 186 formal volunteers aged ≥ 65 years who were recruited from the Keelung chapter of the Buddhist Compassion Relief Tzu Chi Foundation in Taiwan. A hierarchical stepwise linear regression was used to examine the association between scores on the Satisfaction With Life Scale (SWLS) with the Rosenberg Self-Esteem Scale (RSES) and the Hedonic and Eudaimonic Motives for Activities-Revised (HEMA-R) scale. The results showed that SWLS was significantly associated with RSES score (standardized beta (std. β) = 0.199, p = 0.003), the eudaimonic subscale score of the HEMA-R (std. β = 0.353, p < 0.001), a vegetarian diet (std. β = 0.143, p = 0.027), and volunteering for five days or more a week (std. β = 0.161, p = 0.011). In conclusion, improving self-esteem and promoting eudaimonic motives in older adults who are formally volunteering could be effective strategies for enhancing their levels of life satisfaction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".