What Does It Mean to Successfully Age?: Multinational Study of Older Adults’ Perceptions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Successful aging is a mainstay of the gerontological literature, but it is not without criticism, including the often-limited way that it is studied and measured as well as the exclusion of older adults' voices in its formulation and understanding. This study sought to address these issues through a qualitative investigation across multiple countries. RESEARCH DESIGN AND METHODS: This was a mixed-methods, cross-sectional, exploratory study using an online survey. Nations that received the survey included Australia, New Zealand, the United Kingdom, Ireland, Canada, and the Unites States. Participants aged 65 and older were asked to describe what successful aging means to them in an open-ended survey item. Summative content analysis was utilized to examine the responses. RESULTS: Successful aging was defined by 1,994 participants, and 6 themes along with 20 subthemes were found. In contrast to conception that successful aging is solely or predominantly related to the absence of disease and decline, the most prominent theme in this study was "active, independent, and engaged" as the hallmark of success. DISCUSSION AND IMPLICATIONS: Although health and health maintenance were present in other themes, these findings support a multidimensional definition of successful aging that promotes the perspectives of older people. Future research should seek to further investigate the ways in which person-in-environment factors influence definitions of successful aging, including culture, gender and gender identity, race and ethnicity, and socioeconomic background.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".