Bibliographic record
Abstract
For a few years, the image associated with the ageing process has been more positive: expressions such as << successful aging >>, << well aging >> or << healthy aging >> are more frequently used in relation to aging. However, there is still a lack of consensus on this appealing and challenging concept. Therefore, we present an overview of its definition, psychosocial determinants and conceptual models. We report that the meaning of the concept varies according to the cultural context (individualistic/relational societies), to the actors' perspectives (researcher/elderly) and according to the dominant approach (biomedical/holistic). Several models have also been identified: some are specific to a scientific domain and rely on a unique marker of well aging; others are multicriterion and embrace a broader field. Psychosocial factors are the most frequent determinants addressed by models. Among these factors, social and personal resources can be mobilized and learned, contrarily to the less modifiable personality traits. In summary, the << well aging >> framework offers a unique opportunity to identify and to reinforce positive aspects in the aging process. However, the integration of the various models, more complementary than opposite, into only one meta-model remains a task to be done by researchers for a better effectiveness of << well aging >> promotion programs.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".