Healthy Aging-Relevant Goals: The Role of Person–Context Co-construction
Bibliographic record
Abstract
OBJECTIVES: This article considers how individuals' motivation for healthy aging manifests within the myriad of different contexts that older adults are embedded in as they move through later life. METHODS: Drawing on the concept of co-construction, we argue that persons and contexts both contribute to the emergence, maintenance, and disengagement from healthy aging relevant goals in adulthood and old age. RESULTS: To promote the understanding of such co-constructive dynamics, we propose four conceptual refinements of previous healthy aging models. First, we outline various different, often multidirectional, ways in which persons and contexts conjointly contribute to how people set, pursue, and disengage from health goals. Second, we promote consideration of context as involving unique, shared, and interactive effects of socio-economic, social, physical, care/service, and technology dimensions. Third, we highlight how the relevance, utility, and nature of these context dimensions and their role in co-constructing health goals change as individuals move through the Third Age, the Fourth Age, and a terminal stages of life. Finally, we suggest that these conceptual refinements be linked to established (motivational) theories of lifespan development and aging. DISCUSSIONS: In closing, we outline a set of research questions that promise to advance our understanding of the mechanisms by which contexts and aging persons co-construct healthy aging relevant goals and elaborate on the applied significance of this approach for common public health practices.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".