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Record W4416995108 · doi:10.1037/emo0001617

Speaking about flexibility: Age differences in the variability and situational sensitivity of emotion regulation strategies.

2025· article· en· W4416995108 on OpenAlexfundno aff
Ute Kunzmann, Steffen Nestler, Martin Katzorreck-Gierden, Denis Gerstorf, Carsten Wrosch

Bibliographic record

VenueEmotion · 2025
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsSituational ethicsControllabilityCognitionStressorAge groupsCorrelationCognitive aging

Abstract

fetched live from OpenAlex

= 3.50, 61% female) reported their use of cognitive reappraisal and situation modification strategies in relation to their most stressful situation each day. They also rated the perceived controllability of these situations. Analyses revealed multidirectional age differences in the variability of strategy use: Older adults showed greater temporal variability in situation modification but less variability in cognitive reappraisal, compared to younger adults. Additionally, there were significant age differences in how situation modification strategies were adapted to the perceived controllability of stressors. The within-person correlation between stressor controllability and situation modification use was stronger in older adults than in younger adults. In contrast, no such age differences were found for cognitive reappraisal strategies. These effects remained robust even after controlling for various person- and stressor-related characteristics. Overall, our results suggest that age differences in the ability to flexibly adjust emotion regulation strategies to specific situations might depend on the strategy used. Further research should examine additional situational characteristics and emotion regulation strategies. (PsycInfo Database Record (c) 2026 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)0.003
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 score0.358
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.062
GPT teacher head0.371
Teacher spread0.309 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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