“I am African American”: Racial-ethnic self-designation, self-concept, and major depression among African Americans
Bibliographic record
Abstract
BACKGROUND: Research remains mixed on whether racial-ethnic self-designation impacts psychological health and well-being among Americans of African descent. Existing studies mainly use non-representative samples to address this question. Some scholars argue that Black people who express an African-centered identity should experience improved mental health because it enhances one's sense of self. However, what role self-designation may have on depression, one of the most common forms of disability, is largely unknown among African Americans. There is also limited evidence on whether one's self-concept can help us understand the relationship between self-designation and mental health among African Americans. METHODS: = 3,329), I examined whether self-designation as African American, Afro-American, Negro, Black American, or some other label versus Black was associated with self-esteem, mastery, and major depressive episodes. RESULTS: Using OLS models, I found that respondents who preferred the terms African American or Afro-American exhibited higher mastery levels compared to individuals who preferred the label Black, the most common term used among respondents. African American identifying respondents also exhibited significantly higher levels of self-esteem compared to Black identifying individuals. Using logistic regression models, I found that only African American identifying respondents were significantly less likely than Black identifying respondents to meet the criteria for major depressive episodes in the past-year. Higher levels of mastery and self-esteem helped to explain such differences. CONCLUSION: In sum, among Americans of African descent, identification as African American rather than Black may help fight depressive episodes because such self-designation may enhance one's self-concept. Further research is necessary to explore other possible psychological implications of self-designation among the African American/Black population.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".