Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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 it