Anger response styles and blood pressure: At least Don’t Ruminate about it!
Bibliographic record
Abstract
BACKGROUND: Research on anger suggests a link with blood pressure (BP), but the findings are complex and highly variable; this is at least partly attributable to measurement issues. PURPOSE: In this study we used a new model of anger responding that comprises 6 independent anger response styles in 2 dimensions: Aggression, Assertion, Social Support Seeking, Diffusion, Avoidance, and Rumination. Linear and interactive relations between the anger response styles and resting and ambulatory BP were tested, controlling for traditional risk factors and level of hostility. METHODS: Data from 2 samples of different cardiovascular health status were examined. In Study 1, 109 healthy participants (45 men and 64 women) were recruited. Study 2 involved a sample of 159 hypertensive patients (90 men and 69 women). All participants provided demographic and health information; completed the Behavioral Anger Response Questionnaire, a hostility measure; and underwent resting BP measurement. Study 2 participants also provided 24-hr ambulatory BPs. RESULTS: Examination of linear effects revealed inconsistent associations between anger response styles and BP. The moderating effect of Rumination on the relationship between the other anger response styles and BP was examined next. Rumination had a deleterious influence on the relation between Avoidance and Assertion and resting and ambulatory BP levels. The moderating influence of Rumination on Social Support Seeking varied between the genders. CONCLUSIONS: Overall, the results suggest that rumination is a critical moderating variable in the relation of anger and BP.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".