Psychosocial Determinants of Quality of Life and Active Aging. A Structural Equation Model
Bibliographic record
Abstract
Population aging is the 21st century's predominant demographic event. The old-age dependency ratio is projected to rise sharply in the next decades. Variables of health-related quality of life can be useful in designing interventions for promoting active aging to prevent dependency and save governments' budgets. This study aims to find a model capable of explaining how psychosocial variables are related to improved quality of life during active aging, and if this relationship varies with age. Structural equation modelling (SEM) was used to examine the relationships among the availability of social resources, memory, depression, and perception of quality of life from three community senior centers in Madrid (Spain) in a sample of 128 older adult volunteers. The results suggest a psychosocial model where the availability of social support improves quality of life and explicit memory, reduces depression in active older adults, and where there are two main elements for understanding quality of life: perception of health and satisfaction. Importantly, age does not modify the interactions between variables, suggesting that their behavior is constant across aging. We concluded that the availability of social resources, understood not only as the people we interact with daily but also other family members, close friends, or institutions that could help in case of an emergency, allows people to avoid isolation and loneliness, increasing satisfaction and well-being in older adults. Professionals and policymakers should promote well-being by incorporating psychosocial variables related to personal satisfaction in the existential project, not only health, functional activity, or a friendly environment. Older adults need to feel that they are not alone, and in this sense, the availability of social resources is key.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".