Calmness and excitement intensity and variability in old age: Linking stressful circumstances to well-being and health.
Bibliographic record
Abstract
= 7.2). Data were collected in 2018. We examined the effects of calmness and excitement intensity (between- and within-person differences) and variability within the context of stressful experiences on older adults' well-being and health. We expected that levels, increases, and consistency (i.e., low variability) of calmness, but not excitement, may be adaptive, particularly among older adults with low control perceptions. Results from hierarchical and linear regression models showed that calmness intensity was associated with better well-being and health, on both the between- and within-person levels. Between-person levels of excitement intensity, by contrast, predicted poorer health and depressive symptoms among individuals with low perceived control. Compared to variable calmness, consistent calmness was associated with adaptive outcomes, particularly for older adults with low perceived control. By contrast, excitement variability was largely unrelated to well-being and health, except for a positive association with depressive symptoms among adults with low control. Findings inform functional theories of emotion by suggesting that positive emotions with disparate motivational functions can exert diverging effects in older adulthood. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".