The Impact of Racial and Non-racial Discrimination on Health Behavior Change Among Visible Minority Adults During the COVID-19 Pandemic
Bibliographic record
Abstract
INTRODUCTION: Pre-pandemic health behavior has been put forward as a reason for excess COVID-19 infection and death in some racialized groups. At the same time, scholars have labeled racism the other pandemic and argued for its role in the adverse COVID-19 outcomes observed. The purpose of this study was to examine the impact of discrimination on health behavior change among racialized adults in the early stages of the pandemic. METHODS: Data were collected from 210 adults who identified as a visible minority in Alberta, Canada, in June 2020. The Everyday Discrimination Scale (Short Version) was adapted to examine past-month experiences. Four questions asked if alcohol/cannabis use and stress eating had significantly increased, and if sleep and exercise had significantly decreased in the past month. Logistic regression models examined associations between discrimination attributed to racial and non-racial causes and health behavior change adjusted for covariates. RESULTS: The majority of adults (56.2%) reported past-month discrimination including 26.7% who attributed it to their race. Asian adults reported more racial discrimination and discrimination due to people believing they had COVID-19 than other visible minorities. Racial discrimination during the pandemic was strongly associated with increased substance use (OR: 4.0, 95% CI 1.2, 13.4) and decreased sleep (OR: 7.0, 95% CI 2.7, 18.4), and weakly associated with decreased exercise (OR: 2.2, 95% CI 1.1, 4.5). Non-racial discrimination was strongly associated with decreased sleep (OR: 4.8, 95% CI 1.8, 12.5). CONCLUSION: Racial discrimination may have a particularly important effect on intensifying adverse health behavior changes among racialized adults during a time of global crisis.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".