Cardiovascular reactivity during conversations about discrimination is buffered by social support among U.S. Latines
Bibliographic record
Abstract
Racial discrimination is conceptualized as an acute and chronic stressor. Like other acute stressors, lab-based studies demonstrate acute effects of discrimination-related stressors on stress-related cardiovascular outcomes, including total cardiac output, blood pressure, and indices of sympathetic and parasympathetic nervous system activity. Critically, it is important to understand how individual and social factors buffer the experience of race-related acute stress. The current study extends existing work by measuring cardiovascular indices of stress during conversations about racial/ethnic discrimination and examines the moderating role of social support. Latine/Hispanic participants (N = 97) talked about personal discrimination experiences with either a close other or a research assistant they had never previously met. Participants in both conditions exhibited cardiovascular reactivity indicative of stress during the conversation. Additionally, patterns of reactivity reflected a more adaptive stress response and recovery profile when participants talked about discriminatory experiences with a close other relative to a stranger (less parasympathetic withdrawal during the stressor and more parasympathetic rebound during recovery). These patterns are consistent with a stress buffering account of social support, which suggests social bonds and community-level support are critical to consider in interventions to mitigate the harms of experiencing discrimination and prevent chronic health disparities.
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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 | 0.004 |
| 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".