Mediators of the relationship between race and allostatic load in African and White Americans.
Bibliographic record
Abstract
OBJECTIVE: Allostatic load (AL) is a cumulative index of physiological dysregulation, which has been shown to predict cardiovascular events and all-cause mortality. On average, African Americans (AA) have higher AL than their White American (WA) counterparts. This study investigated whether differences in discrimination, negative affect-related variables (e.g., experience and expression of anger, depression), and health practices (e.g., exercise, alcohol use, smoking, subjective sleep quality) mediate racial differences in AL. METHOD: Participants included healthy, AA (n = 76) and WA (n = 100), middle-aged (Mage = 35.2 years) men (n = 98) and women (n = 78). Questionnaires assessed demographics, psychosocial variables, and health practices. Biological data were collected as part of an overnight hospital stay-AL score was composed of 11 biomarkers. The covariates age, gender, and socioeconomic status were held constant in each analysis. RESULTS: Findings showed significant racial differences in AL, such that AA had higher AL than their WA counterparts. Results of serial mediation indicated a pathway whereby racial group was associated with discrimination, which was then associated with increased experience of anger and decreased subjective sleep quality, which were associated with AL (e.g., race → discrimination → experience of anger → subjective sleep quality → AL); in combination, these variables fully mediated the relationship between race and AL (p < .05). CONCLUSION: These results suggest that discrimination plays an important role in explaining racial differences in an important indictor of early disease through its relationship with negative affect-related factors and health practices. (PsycINFO Database Record
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".