Ethnic Identification and Stress: Examining HPA Axis Reactivity in Response to a Discrimination Related Laboratory Stressor
Bibliographic record
Abstract
Through processes of colonization, American Indians and Alaskan Natives (AI/AN) have faced continuous, ongoing efforts to diminish their unique ethnic identities. Maintaining a robust ethnic identity may shield against harms related to colonization, such as ethnic discrimination. Ethnic discrimination is a psychosocial stressor with implications for the reactivity of our main stress axis, the hypothalamic-pituitary-adrenal (HPA) axis. However, whether ethnic identification buffers against ethnic discrimination remains unclear. The current study examined the stress-buffering effects of two facets of ethnic identification – exploration and commitment – on HPA reactivity in 303 urban-dwelling AI/AN using samples of salivary cortisol collected throughout a discrimination-based stressor task. Building on the rejection-identification hypothesis and other related theories, we hypothesized that ethnic identity exploration will predict greater HPA axis reactivity and commitment will predict lesser HPA axis reactivity. Findings revealed that exploration and commitment were both positively related to stress levels, contrary to expectations that commitment would weaken the relationship between discrimination and stress. Results align with broader findings suggesting ethnic identity can intensify stress responses to discrimination. These findings contribute to a deeper understanding of discrimination-related stress and ethnic identification as they relate to ongoing health disparities and challenges experienced by AI/AN communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".