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Record W4408997667 · doi:10.1002/smi.70030

Savouring Moderates Affect Reactivity to Daily Events in Old Age

2025· article· en· W4408997667 on OpenAlexaff
Shira Peleg, Miriam Wallimann, Theresa Pauly

Bibliographic record

VenueStress and Health · 2025
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAffect (linguistics)PsychologyReactivity (psychology)Clinical psychologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This study investigated savouring as a moderator of affect reactivity to daily events among older adults. METHOD: A sample of 108 individuals aged 65-92 years (M = 73.11, SD = 5.92; 58% women) completed daily diary questionnaires over 14 days, reporting on daily stressors, positive events, savouring, and positive and negative affect. RESULTS: Multilevel models showed that on days when a stressor was experienced, negative affect was higher when daily savouring (within-person) was low, but this association was not observed when daily savouring was high. Additionally, on days with positive events, negative affect was lower when trait savouring (between-person) was high, but this effect was not found when trait savouring was low. DISCUSSION: The findings highlight the importance of savouring as a key factor in managing emotional responses to daily experiences among older adults. Specifically, deliberately engaging with positive experiences might buffer daily negative emotional responses.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.462
Teacher spread0.382 · 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 source (direct Gemma or distilled Codex), 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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