Association Between Discrimination and Depressive Symptoms Among Hispanic or Latino Adults During the COVID-19 Pandemic: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Discrimination and xenophobia toward Hispanic and Latino communities increased during the COVID-19 pandemic, likely inflicting significant harm on the mental health of Hispanic and Latino individuals. Pandemic-related financial and social instability has disproportionately affected Hispanic and Latino communities, potentially compounding existing disparities and worsening mental health. OBJECTIVE: This study aims to examine the association between discrimination and depressive symptoms during the COVID-19 pandemic among a national sample of Hispanic and Latino adults. METHODS: Data from a 116-item web-based nationally distributed survey from May 2021 to January 2022 were analyzed. The sample (N=1181) was restricted to Hispanic or Latino (Mexican or Mexican American, Puerto Rican; Cuban or Cuban American, Central or South American, and Dominican or another Hispanic or Latino ethnicity) adults. Depression symptoms were assessed using the 2-item Patient Health Questionnaire. Discrimination was assessed using the 5-item Everyday Discrimination Scale. A multinomial logistic regression with a block entry model was used to assess the relationship between discrimination and the likelihood of depressive symptoms, as well as examine how controls and covariates affected the relationship of interest. RESULTS: Mexican or Mexican American adults comprised the largest proportion of the sample (533/1181, 45.13%), followed by Central or South American (204/1181, 17.3%), Puerto Rican (189/1181, 16%), Dominican or another Hispanic or Latino ethnicity (172/1181, 14.6%), and Cuban or Cuban American (83/1181, 7.03%). Approximately 31.26% (367/1181) of the sample had depressive symptoms. Regarding discrimination, 54.56% (634/1181) reported experiencing some form of discrimination. Compared with those who did not experience discrimination, those who experienced discrimination had almost 230% higher odds of depressive symptoms (adjusted odds ratio [AOR] 3.31, 95% CI 2.42-4.54). Also, we observed that sociodemographic factors such as age and gender were significant. Compared with participants aged 56 years and older, participants aged 18-35 years and those aged 36-55 years had increased odds of having depressive symptoms (AOR 3.83, 95% CI 2.13-6.90 and AOR 3.10, 95% CI 1.74-5.51, respectively). Women had higher odds of having depressive symptoms (AOR 1.67, 95% CI 1.23-2.30) than men. Respondents with an annual income of less than US $25,000 (AOR 2.14, 95% CI 1.34-3.41) and US $25,000 to less than US $35,000 (AOR 1.89, 95% CI 1.17-3.06) had higher odds of depressive symptoms than those with an annual income of US $50,000 to less than US $75,000. CONCLUSIONS: Our findings provide significant importance especially when considering the compounding, numerous socioeconomic challenges stemming from the pandemic that disproportionately impact the Hispanic and Latino communities. These challenges include rising xenophobia and tensions against immigrants, inadequate access to mental health resources for Hispanic and Latino individuals, and existing hesitations toward seeking mental health services among this population. Ultimately, these findings can serve as a foundation for promoting health equity.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".