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Record W1948301995 · doi:10.1177/0165025415597549

Goal disengagement capacities and severity of disease across older adulthood

2015· article· en· W1948301995 on OpenAlexaff
Joelle Jobin, Carsten Wrosch

Bibliographic record

VenueInternational Journal of Behavioral Development · 2015
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsDisengagement theoryPsychologyMediationDistressLongitudinal studyStressorAssociation (psychology)Young adultDepressive symptomsDevelopmental psychologyDiseaseClinical psychologyEmotional distressGerontologyPsychiatryMedicineCognitionAnxietyInternal medicine

Abstract

fetched live from OpenAlex

This study examined age-related associations between goal disengagement capacities, emotional distress, and disease severity across older adulthood. Given that an age-related increase in the experience of stressors might render important goals unattainable, it is expected that goal disengagement capacities would predict a decrease in the severity of experienced illness (i.e., the common cold) by preventing emotional distress (i.e., depressive symptoms), particularly so among individuals in advanced (as compared to early) old age. This hypothesis was tested in a 6-year longitudinal study of 131 older adults (age range = 64 to 90). Regression analyses showed that goal disengagement capacities buffered 6-year increases in older adults’ cold symptoms, and that this effect was significantly pronounced among older-old participants. Mediation analyses further indicated that changes in depressive symptoms exerted an indirect effect on the age-related association between goal disengagement and changes in cold symptoms. The study’s findings suggest that goal disengagement capacities become increasingly important for protecting emotional well-being and physical health as older adults advance in age.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.089
GPT teacher head0.431
Teacher spread0.342 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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