Satisfaction with social support in older adulthood: The influence of social support changes and goal adjustment capacities.
Bibliographic record
Abstract
A 6-year longitudinal study of 180 older adults (M age at baseline = 72.12 years) examined whether goal adjustment capacities (i.e., goal disengagement and goal reengagement) moderate the associations between transient and long-term longitudinal changes in social support partners with social support satisfaction. Results from hierarchical linear models show that high levels of, and increases in, goal disengagement capacities buffered the adverse effect of transient declines in perceptions of social support partners on satisfaction with social support. Moreover, increases in goal disengagement buffered the effect of long-term longitudinal declines in perceived social support on reduced levels of social support satisfaction. However, when participants perceived longitudinal increases in the number of social support partners, low levels of, and declines in, goal reengagement capacities were associated with high levels or increases in social support satisfaction. This pattern of findings suggests that goal disengagement can ameliorate social support satisfaction if older adults perceive a reduction in their social support network. Withdrawing from engagement in new goals, by contrast, may contribute to social support satisfaction if older adults perceive an increase in the number of social support partners.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".