The Association of Unfairness with Mental and Physical Health in a Multiethnic Sample of Adults: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Two psychosocial constructs that have shown consistent associations with negative health outcomes are discrimination and perceived unfairness. OBJECTIVE: The current analyses report the effects of discrimination and unfairness on medical, psychological, and behavioral outcomes from a recent cross-sectional survey conducted in a multiethnic sample of adults in Michigan. METHODS: A cross-section survey was collected using multiple approaches: community settings, telephone-listed sample, and online panel. Unfairness was assessed with a single-item previously used in the Whitehall study, and everyday discrimination was assessed with the Williams 9-item scale. Outcomes included mental health symptoms, past-month cigarette use, past-month alcohol use, past-month marijuana use, lifetime pain medication use, and self-reported medical history. RESULTS: A total of 2238 usable surveys were collected. In bivariate analyses, higher unfairness values were significantly associated with lower educational attainment, lower age, lower household income, and being unmarried. The highest unfairness values were observed for African American and multiracial respondents followed by Middle Eastern or North African participants. Unfairness was significantly related to worse mental health functioning, net adjustment for sociodemographic variables, and everyday discrimination. Unfairness was also related to self-reported history of depression and high blood pressure although, after including everyday discrimination in the model, only the association with depression remained significant. Unfairness was significantly related to 30-day marijuana use, 30-day cigarette use, and lifetime opiate use. CONCLUSIONS: Our findings of a generally harmful effect of perceived unfairness on health are consistent with prior studies. Perceived unfairness may be one of the psychological pathways through which discrimination negatively impacts health. Future studies examining the relationships we observed using longitudinal data and including more objective measures of behavior and health status are needed to confirm and extend our findings.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".