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Record W2514044123 · doi:10.1093/geronb/gbw110

A Longitudinal Assessment of Perceived Discrimination and Maladaptive Expressions of Anger Among Older Adults: Does Subjective Social Power Buffer the Association?

2016· article· en· W2514044123 on OpenAlexaff
Yeonjung Lee, Alex Bierman

Bibliographic record

VenueThe Journals of Gerontology Series B · 2016
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAssociation (psychology)AngerPsychologyBuffer (optical fiber)Clinical psychologyDevelopmental psychologyPsychotherapistComputer science

Abstract

fetched live from OpenAlex

Objectives: We examine whether perceived discrimination in older adults is associated with external conflict (anger-out) and internally directed anger (anger-in), as well as how subjective social power-as indicated by a sense of personal control and subjective social status-modifies these associations while holistically controlling for time-stable confounds and the five major dimensions of personality. Method: The 2006 and 2008 psychosocial subsamples of the Health and Retirement Study were combined to create baseline observations, and the 2010 and 2012 waves were combined to create follow-up observations. Responses were analyzed with random-effects models that adjust for repeated observations and fixed-effects models that additionally control for all time-stable confounds. Results: Discrimination was significantly associated with anger-in and anger-out. Fixed-effects models and controls for personality reduced these associations by more than 60%, although they remained significant. Measures of subjective social power weaken associations with anger-out but not anger-in. Discussion: The mental health consequences of perceived discrimination for older adults may be over-estimated if time-stable confounds and personality are not taken into account. Subjective social power can protect victims of discrimination from reactions that may escalate conflict, but not from internalized anger that is likely to be wearing and cause further health problems.

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 score0.030
Threshold uncertainty score0.927

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 studies0.0000.001
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 head0.036
GPT teacher head0.374
Teacher spread0.339 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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