Racial/Ethnic Differences in COVID-19-Traumatic Symptoms, Sleep, Coping Outcomes in a Group of New-Yorkers
Bibliographic record
Abstract
Abstract Background Little research has examined within/between group predictors and mediators of race/ethnic differences or disparities in mental and sleep health outcomes arising from the experience of the COVID-19 pandemic. Objectives This study sought to evaluate the effect of COVID-19 experiences on trauma-related symptoms and sleep quality among a multiracial/ethnic sample in New York. Method This is a cross-sectional study conducted online among multiethnic adults (n=541) who experienced the pandemic in New York from September to November 2020. Comparisons of characteristics and mean scores by race/ethnicity status were conducted using one-way ANOVA and independent samples t-tests for continuous variables and chi-square tests for categorical variables. Multilinear regression was used for associations between social determinants of health and/or SES, trauma-related symptoms, coping, and sleep. Results Compared to Whites [Mean (SD)= (24.1(7.6)] and other group [Mean (SD)=24.9(8.2), Blacks [Mean (SD)=(26.3(6.4)] and Hispanics [Mean(SD)=(27.2(8.2)] reported higher level of peritraumatic distress [df= 3; F=4273; p=0.005). The prevalence of clinically significant PTSD symptoms was 21.4%(n=113): [Whites=31(16.3%); Blacks=28(25.7%); Hispanics=24(25%); and other groups=30(22.4%); x2 =4.93; p=0.177]. This rate doubled [48.3%(257)] when it comes to the overall clinically significant depression level. Compared to all subcategories, [Blacks=52(47.7%); Hispanics =62(64.6%); other group=66(49.3%)], depression symptoms were lower among Whites [77(39.9%; x 2 =15.71; p =0.001]. We found a prevalence of insufficient sleep <6 hours of 41%(198): [Whites=69(39.4%); Blacks=43(41.7%); Hispanics=46(52.3%); other groups=40(34.2%); x 2=12.21; p =0.057]. Several unique demographic predictors of PTSD emerged for distinct racial/ethnic groups. Among Blacks, sex [β = −0.22; p < .01] and employment [β = −0.159; p < .05] emerged as significant predictors for PTSD, but for no other racial/ethnic group. Interestingly, among Hispanics [β = −0.144; p = .064] and Blacks [β = −0.174; p = .0.076], coping strategies did not mitigate PTSD or depressive symptoms. Conclusion As New York and the rest of the world are trying to bounce back from the COVID-19 consequences, mental health outcomes are devastating, particularly among historically marginalized communities. This study provides insight into the emergency for policymakers to invest in racial justice programs and provide free access to culturally responsive mental health care for the most vulnerable groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".