Applying Sociocultural Models of Aging to Promote Optimal Health and Wellbeing Amongst Aging Populations: A Literature Review on Interdisciplinary Approaches to Mental Health Care
Bibliographic record
Abstract
With a significant increase in aging populations in the coming years affecting many factors from increased health spending to restructuring of public spaces, challenges of how society can adapt to this major population change persist [1][2][3][4].Sociocultural models of aging have been shown widely effective in a variety of contexts to describe processes of aging, theories to guide future experiments, and applications of theories into practice [5][6][7].A review describing Bronfenbrenner's ecological perspective, the PPCT model, and the life course perspective is presented.Applications to research in the field of mental health gerontology, future aging research, and criticisms of theories are described.There is a focus on depression, dementia, treating the whole person, and cultural competency.Considering the variety of factors that influence an individual's risk and experience in living with a mental health issue, including their lived experience and personal preferences, holds important considerations for promoting optimal health and well-being in aging successfully.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".