A Systematic Review of Black People Coping With Racism: Approaches, Analysis, and Empowerment
Bibliographic record
Abstract
This article reviews the current research literature concerning Black people in Western societies to better understand how they regulate their emotions when coping with racism, which coping strategies they use, and which strategies are functional for well-being. A systematic review of the literature was conducted, and 26 studies were identified on the basis of a comprehensive search of multiple databases and reference sections of relevant articles. Studies were quantitative and qualitative, and all articles located were from the United States or Canada. Findings demonstrate that Black people tend to cope with racism through social support (friends, family, support groups), religion (prayer, church, spirituality), avoidance (attempting to avoid stressors), and problem-focused coping (confronting the situation directly). Findings suggest gender differences in coping strategies. We also explore the relationship between coping with physical versus emotional pain and contrast functional versus dysfunctional coping approaches, underscoring the importance of encouraging personal empowerment to promote psychological well-being. Findings may help inform mental-health interventions. Limitations include the high number of American-based samples and exclusion of other Black ethnic and national groups, which is an important area for further exploration.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".