Psychosocial aspects of successful ageing and resilience: critique, integration and implications / Aspectos psicológicos del envejecimiento exitoso y la resiliencia: crítica, integración e implicaciones
Bibliographic record
Abstract
As the number of older adults increases worldwide, it is becoming increasingly important to find effective ways of fostering better ageing trajectories. The models used to shape this process inform research, policy and practice and impact older adults themselves. Two important ageing models are successful ageing (SA) and resilience (RES). Aligning the conceptual framework in research contexts with those of older adults’ perspectives is an integral component of driving forward the research agenda in a manner that has the greatest potential to benefit older adults. Studies conducted with laypersons indicate that psychosocial components are important components of successful ageing models; therefore, it is imperative that these non-biomedical components are incorporated. There are many similarities between SA and RES models, but an important distinguishing feature is the incorporation of adversity into conceptualizations of resilience. SA models suggest high levels of functioning as a requirement for ageing successfully, regardless of the circumstances the individual experiences; resilience models take into account the level of adversity being experienced by the individual. Individuals can demonstrate RES by having a more positive outcome than would be expected given their level of adversity. The incorporation of psychosocial constructs into SA models and the integration of SA and RES paradigms has important implications for research and for older adults themselves. Through the promotion of models of ageing that include psychosocial components and elements of adversity, greater generalizability to a broader population is possible with enhanced potential for research derived from these efforts to more positively influence individuals’ trajectories of ageing.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.014 |
| 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".