EFFECTS OF SADNESS AND ANGER ON OLDER ADULTS’ GOAL DISENGAGEMENT CAPACITIES: THE ROLE OF PHYSIOLOGICAL STRESS
Bibliographic record
Abstract
Goal disengagement is an adaptive self-regulation process that has been linked to improved levels of older adults’ well-being and health. In addition, it has been suggested that specific negative emotions, such as sadness, may promote goal disengagement processes. By contrast, other negative emotions, such as anger, may make disengagement more difficult. Such differential effects of emotions on individuals’ self-regulation capacities may be observed particularly among older adults who experience severe, uncontrollable stressors, which could be indicated by generally high levels of diurnal cortisol. In the present study, we examined these hypotheses by testing within-person associations between specific emotions and goal disengagement capacities. In addition, we examined whether the obtained within-person effects were moderated by between-person differences in physiological stress. Data were drawn from a 10-year longitudinal sample of 184 community-dwelling older adults (Mage = 72.08, SDage = 5.70). At each of six waves, participants’ physiological stress (i.e., cortisol), sadness, anger, and goal disengagement capacities were assessed. Hierarchical linear modeling indicated that among older adults with generally higher levels of physiological stress (between-person), experiences of sadness, but not anger, predicted higher levels of goal disengagement capacities (within-person). This pattern was not found among participants with generally lower levels of physiological stress. These findings contribute to theoretical accounts that conceptualize the roles of discrete emotions in affecting older adults’ self-regulation processes. The study identifies a new pathway to successful aging, indicating that the experience of sadness could promote adaptive goal disengagement capacities in the context of older adults’ stressful life circumstances.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".