Perceived Discrimination and Physical and Mental Health Among Resettled Communities in Ohio
Bibliographic record
Abstract
Abstract This study examined perceived discrimination and its relationship with health outcomes, including chronic illnesses and mental health problems, among refugees resettled in Ohio. Using a cross-sectional design, an online survey was conducted from November 2023 to February 2024, with a total sample of 478. Participants were adults (≥18) from five major refugee communities in Ohio: Afghan, Bhutanese, Congolese, Ethiopian/Eritrean, and Somali. Perceived discrimination was measured using the everyday discrimination Scale (EDS), while mental health symptoms were assessed with the PHQ-4. Chronic morbidity was captured through self-reported physician-diagnosed conditions. Multivariable logistic regression models were used to assess relationships between discrimination and health outcomes, adjusting for demographic, social, and healthcare access variables. Approximately 28.8% reported having at least one chronic condition, and 23.0% screened positive for mental health problems. Perceived discrimination was prevalent, with over a quarter of participants reporting experiences of discrimination on each item of the EDS. Notably, 52.1% reported being treated with less courtesy or respect. Over one-third (33.5%) of participants reported experiencing racial discrimination. Perceived discrimination was significantly associated with adverse mental health outcomes (adjusted odds ratio [aOR] = 1.34; 95% CI: 1.10–1.62). Participants who reported experiencing two or more types of discrimination had 2.02 times higher odds of chronic morbidity (aOR = 2.02; 95% CI: 1.14–3.58) and 2.68 times higher odds of mental health problems (aOR = 2.68; 95% CI: 1.45–4.95) compared to those who did not report discrimination. Perceived discrimination emerges as a critical determinant of health disparities among refugee populations, reflecting significant associations with chronic morbidity and mental health issues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".