Bibliographic record
Abstract
OBJECTIVES: Successful aging continues to be applied in a variety of contexts and is defined using a number of different constructs. Although previous reviews highlight the multidimensionality of successful aging, a few have focused exclusively on non-biomedical factors, as was done here. METHODS: This scoping review searched Ovid Medline database for peer-reviewed English-language articles published between 2006 and 2015, offering a model of successful aging and involving research with older adults. RESULTS: Seventy-two articles were reviewed. Thirty-five articles met the inclusion criteria. Common non-biomedical constructs associated with successful aging included engagement, optimism and/or positive attitude, resilience, spirituality and/or religiosity, self-efficacy and/or self-esteem, and gerotranscendence. DISCUSSION: Successful aging is a complex process best described using a multidimensional model. Given that the majority of elders will experience illness and/or disease during the life course, public health initiatives that promote successful aging need to employ non-biomedical constructs, facilitating the inclusion of elders living with disease and/or disability.
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.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".