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Record W4200156969 · doi:10.1093/geroni/igab046.823

Age-Related Change in the Daily Stressor Reactivity Across 20 Years of Adulthood

2021· article· en· W4200156969 on OpenAlexaff
David M. Almeida, Jacqueline Mogle, Jonathan Rush

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsStressorReactivity (psychology)PsychologyLongitudinal studyGerontologyYoung adultClinical psychologyEveryday lifeLongitudinal dataDevelopmental psychologyMental healthActivities of daily livingDemographyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Affective reactivity to everyday stressful events has been shown to be an important predictor of poor mental and physical health. The purpose of this study was to examine longitudinal changes in daily stress across 30 years of adulthood as a critical first step for understanding aging-related trends in daily stress. We used data from the National Study of Daily Experiences (NSDE) to calculate exposure and reactivity to daily stressors collected during telephone interviews over the course of 8 consecutive days. These daily assessment bursts were conducted in 1997, 2007, and 2018. Data were comprised of 33,931 daily interviews from 2,880 adults ages 25-74 at the first burst. Results indicated decreased stressor reactivity over time but this decrease was greater for younger adults. Discussion will focus on how examining change in daily stress processes is critical for illuminating stress and health.

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)0.002
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.395
Teacher spread0.329 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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