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Record W4224129071 · doi:10.1037/emo0001091

Discrete negative emotions and goal disengagement in older adulthood: Context effects and associations with emotional well-being.

2022· article· en· W4224129071 on OpenAlexfundno aff
Meaghan Barlow, Carsten Wrosch, Jeremy Hamm, Tehila Sacher, Gregory E. Miller, Ute Kunzmann

Bibliographic record

VenueEmotion · 2022
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchDeutsche Forschungsgemeinschaft
KeywordsSadnessDisengagement theoryPsychologyAngerContext (archaeology)Developmental psychologyPsycINFOLatent growth modelingMultilevel modelClinical psychologyAffect (linguistics)GerontologyMedicineMEDLINE

Abstract

fetched live from OpenAlex

= 5.70). Participants' sadness, anger, goal disengagement capacity, perceived stress, diurnal cortisol levels, emotional well-being (i.e., positive and negative affect), and sociodemographic variables were assessed at each wave. Hierarchical linear modeling showed that within-person increases in sadness, but not anger, predicted increased goal disengagement capacity among older adults who generally secreted high levels of cortisol. Moreover, older adults' who disengaged more easily when they felt sad were protected from declines in positive affect during assessments in which they secreted high, but not low, levels of cortisol. The study's findings suggest that generally enhanced cortisol output may facilitate an association between sadness and older adults' goal disengagement capacity and that this process may protect against declines in emotional well-being. (PsycInfo Database Record (c) 2022 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 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 score0.004
Threshold uncertainty score0.013

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.287
Teacher spread0.278 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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