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Record W4410302413 · doi:10.1037/emo0001535

Calmness and excitement intensity and variability in old age: Linking stressful circumstances to well-being and health.

2025· article· en· W4410302413 on OpenAlexfundno aff
Parisa Sepehri, Ute Kunzmann, Carsten Wrosch

Bibliographic record

VenueEmotion · 2025
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsCalmnessPsychologyWell-beingDevelopmental psychologyClinical psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

= 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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.349
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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