Resilient Aging: Psychological Well-Being and Social Well-Being as Targets for the Promotion of Healthy Aging
Bibliographic record
Abstract
Population aging is one of the most important social trends of the 21st century and in the United States, the number of people aged ≥65 is projected to increase by nearly 50% in the next 15 years. Most biomedical and public health efforts have focused on reducing harmful risk factors when targeting chronic disease-an approach that has contributed greatly to prevention and treatment programs. However, evidence suggests that the number of years lost to disability is increasing and historic gains we have made in life expectancy are eroding, and even reversing in some groups. As our society ages and grapples with these issues, expanding the focus to include resilience, as well as psychosocial assets in our prevention and treatment programs might help inform the multidisciplinary response effort we need. Here we synthesize research evaluating associations between different dimensions of psychological well-being (e.g., purpose in life, optimism, life satisfaction) and social well-being (e.g., structural, functional, quality) with chronic conditions. We also evaluate evidence around three biopsychosocial pathways hypothesized to underlie these associations. These factors are meaningful, measurable, and potentially modifiable; thus, further pursuing this line of inquiry might unveil innovative paths to enhancing the health of our rapidly aging society.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".