Environmental and capacity drivers of health function: a full perspective of the healthy aging framework
Bibliographic record
Abstract
Objectives: This paper explores how health capacity and environmental factors work together on health function among Chinese older adults. Methods: = 2,688). Individual, family and social environments were assessed as mediators or moderators in the relationship between health capacity and health function. Structural equation models (SEM) were used to examine the pathways of determinants to health function. Results: We found that an individual's capacities and environmental factors had associations with health function in aging settings. Specifically, older adults who reported higher scores in health capacities, age-friendly family and social environment, and higher level of individual life expectation were more likely to experience higher level of health fucntion. Regarding the pathway to health function, age-friendly family (APGAR) and social environments acted as moderators in the relationship between health capacity and health function. The indirect way of health capacity (path coef. =0.269) is stronger than the direct way (path coef. =0.079). The role of social environment (path coef. =0.409) is the predominant in these pathways. Discussion: This study suggests that both capacity and environmental factors are vital to maintain older adults' health function. And the construction of an age-friendly environment, especially social environment, contributes a lot to healthy aging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".