Associations Between Everyday Discrimination and Sleep: Tests of Moderation by Ethnicity and Sense of Purpose
Bibliographic record
Abstract
BACKGROUND: Everyday discrimination holds pernicious effects across most aspects of health, including a pronounced stress response. However, work is needed on when discrimination predicts sleep outcomes, with respect to potential moderators of these associations. PURPOSE: The current study sought to advance the past literature by examining the associations between everyday discrimination and sleep outcomes in an ethnically diverse sample, allowing tests of moderation by ethnic group. We also examined the role of sense of purpose, a potential resilience factor, as another moderator. METHODS: Participants in the Hawaii Longitudinal Study of Personality and Health (n = 758; 52.8% female; mage: 60 years, sd = 2.03) completed assessments for everyday discrimination, sleep duration, daytime dysfunction due to sleep, sleep quality, and sense of purpose. RESULTS: In the full sample, everyday discrimination was negatively associated with sleep duration, sleep quality, and sense of purpose, while positively associated with daytime dysfunction due to sleep. The associations were similar in magnitude across ethnic groups (Native Hawaiian, White/Caucasian, Japanese/Japanese-American), and were not moderated by sense of purpose, a potential resilience factor. CONCLUSIONS: The ill-effects on health due to everyday discrimination may operate in part on its role in disrupting sleep, an issue that appears to similarly impact several groups. The current research extends these findings to underrepresented groups in the discrimination and sleep literature. Future research is needed to better disentangle the day-to-day associations between sleep and discrimination, and identify which sources of discrimination may be most problematic.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".