Effects of racial discrimination tasks on salivary cortisol reactivity among racially minoritized groups: A meta-analysis
Bibliographic record
Abstract
RATIONALE: Racial discrimination can be specifically defined as the differential treatment of an individual or group based on their racial identity. Increasingly, the experience of racial discrimination is characterized as a social stressor that elicits physiological responses and increases the risk of poor health over the lifespan. However, recent attempts at synthesizing the literature surrounding racial discrimination and cortisol output, specifically, have produced mixed results. This is likely due to the inclusion of broad research designs (e.g., cross-sectional, experimental), measures of cortisol activity, and measures of racial discrimination. OBJECTIVES: The primary goal of the current study was to apply a narrow lens to the synthesis of the racial discrimination and cortisol output literature. Specifically, by examining the association between acute racial discrimination and salivary cortisol reactivity, we aimed to demonstrate a clear pattern within the research area. RESULTS: Using five studies (composed of seven unique datasets; N = 650), we conducted a random effects model using the CMA software. Results indicate that racial discrimination stressors are associated with elevated acute cortisol levels (standard difference in means = 0.189, 95 % CI [0.083, 0.295]). CONCLUSION: By approaching the literature with a narrow lens, this meta-analysis provides support for the association between acute experiences of racism and salivary cortisol reactivity. Findings also demonstrate a clear need for future research and highlight the influence of methodological decisions on synthesis efforts.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.037 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".