Racial Discrimination during the COVID-19 Pandemic and Mental Health of Young Adults
Bibliographic record
Abstract
With the COVID-19 pandemic, there has been an increase in mental health problems in the population worldwide. During the pandemic, individuals from East Asian backgrounds have been blamed for COVID-19 and faced xenophobic attacks, leading to increased incidents of racial discrimination. We administered an online survey to examine (a) associations between in-person and online racial discrimination and mental health (i.e., anxiety and depression) among East Asian university students (n=169) in Canada; and (b) the extent to which coping strategies and ethnic/cultural identity stage (e.g., exploration, resolution, affirmation) moderate the associations between discrimination and mental health. Results from hierarchical regressions indicated that experiencing online racial discrimination predicted more anxiety (b= .263, SE= .070, p< .001) and depression (b= .296, SE= .073, p< .001) symptoms. Using emotion-focused disengagement coping strategies predicted more anxiety (b= .705, SE= .129, p< .001) and depression (b= .763, SE= .127, p< .001). However, identity affirmation (i.e., positive feelings towards ethnic group) predicted less depression (b= -.533, SE= .245, p= .031). Results suggest that exposure to online racial discrimination during the pandemic has a negative effect on East Asian students’ well-being. However, positive feelings towards one’s ethnic identity may protect against mental health problems related to experiences of racial discrimination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".