A Longitudinal Assessment of Perceived Discrimination and Maladaptive Expressions of Anger Among Older Adults: Does Subjective Social Power Buffer the Association?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".