Evolving perspectives on successful aging in Singapore: Multigenerational insights from focus groups
Bibliographic record
Abstract
Abstract Understanding what successful aging means to future older adults (currently aged 30s-50s) is crucial for providing relevant support that enables them to age successfully. Their expectations and needs may vary from those of current older adults (60+), shaped by different socio-economic and cultural contexts. While most research in successful aging focuses on current older adults, limited work has been done among the younger generations. Our study aimed to fill this gap by summarizing novel insights on successful aging from multiple generations of adults. Our study engaged 35 participants, and they were from diverse age groups (30s to 60s and over), genders, income levels, and ethnicities (Chinese, Malay, Indian). Each participant completed one focus group discussion which lasted up to 90-minutes. We used semi-structured questions, scenarios, and descriptions of community-based programs around Asia. We conducted inductive thematic analysis of the data. Successful aging involves embracing circumstances: 1. Everchanging life journey, 2. Unconventional family structure, and 3. Emerging technology. First, everchanging life journey is impacted by various factors such as health, finances, and social roles. Second, unconventional family structure is shaped by younger generations’ different perspectives towards marriage, children, and living arrangements. Third, emerging technology such as artificial intelligence, applications, and robots is being used to advance financial planning, health management, and social connectedness. Younger generations in our study perceived successful aging as embracing three circumstances, i.e., everchanging life journey, unconventional family structure, and emerging technology. A series of future studies is warranted to inform services providers about these three circumstances and to facilitate them to use these insights to develop new programs. This is to optimize the future of successful aging among our younger generations. Key messages • Understanding younger generations’ needs is key to tailoring support for successful aging. • Future research could consider their evolving perspectives and needs to recommend appropriate public health support in their aging process.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".