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Record W4391655136 · doi:10.1037/emo0001333

Too little, too much, and “just right”: Exploring the “goldilocks zone” of daily stress reactivity.

2024· article· en· W4391655136 on OpenAlexaff
Jonathan Rush, Anthony D. Ong, Jennifer R. Piazza, Susan T. Charles, David M. Almeida

Bibliographic record

VenueEmotion · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Victoria
FundersCollege of Pharmacy, University of MichiganNational Institute on AgingNational Institutes of HealthUniversity of Michigan
KeywordsGoldilocks principleReactivity (psychology)StressorPsychological resiliencePsychologyHormesisStress (linguistics)Mental healthDevelopmental psychologyClinical psychologySocial psychologyMedicineOxidative stressInternal medicinePsychiatryBiology

Abstract

fetched live from OpenAlex

= 12.12, age range: 35-86; 57% female) completed telephone interviews detailing their stressors and affect on eight consecutive evenings. A series of multilevel structural equation models estimated within-person associations between daily stressors and negative affect (i.e., stress reactivity), and between-person linear and quadratic effects of stress reactivity on mental and physical health outcomes (i.e., life satisfaction, psychological distress, and number of chronic conditions). Findings reveal a significant quadratic effect for each outcome, indicating a U-shaped pattern (inverse U for positively valenced life satisfaction), such that low and high levels of stress reactivity were associated with poorer health and well-being, whereas moderate levels of daily stress reactivity predicted better health outcomes. These findings suggest that individuals who display either very low- or very high-stress reactivity may benefit from interventions that target their emotion regulation skills and coping resources. (PsycInfo Database Record (c) 2024 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.054
GPT teacher head0.314
Teacher spread0.260 · 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

Citations13
Published2024
Admission routes1
Has abstractyes

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