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Record W4417415242 · doi:10.1007/s10902-025-00991-4

Intrapersonal and Interpersonal Regulation of Positive Affect: The Moderating Effects of Stressor Intensity and Perceived Controllability

2025· article· en· W4417415242 on OpenAlexaff
Tracy K. Y. Wong, Kai Zhao

Bibliographic record

VenueJournal of Happiness Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsIntrapersonal communicationStressorAffect (linguistics)Interpersonal communicationMultilevel modelAssociation (psychology)Quality of Life ResearchInterpersonal relationship

Abstract

fetched live from OpenAlex

Abstract The association between positive affect regulation and well-being has often been examined at the intrapersonal level, but positive affect regulation may also occur at the interpersonal level and be shaped by contextual factors. Thus, positive affect regulation strategies, including savoring and dampening, were examined in relation to well-being (i.e. positive affect and life satisfaction) at the intra- and inter-personal levels, while also considering the moderating effects of stressor intensity and perceived controllability. Participants included 281 emerging adults ( M age = 21.29; 71% female-identifying), who responded to a 14-day daily diary. Multilevel analyses indicated that individuals reported lower well-being on days when they engaged more in dampening, but greater well-being on days when they engaged more in emotion-focused and self-focused positive rumination. Co-savoring was also positively associated with well-being, but co-dampening was not. Perceived controllability, but not stressor intensity, moderated the associations of dampening, self-focused positive rumination, and co-savoring with life satisfaction. These findings suggest that regulating positive affect independently and with social others has implications for well-being, and that contextual factors also matter.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.295
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.342
Teacher spread0.322 · 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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