Study Protocol: Racial Discrimination, Cultural Processes and Wellbeing Among Asian University Students
Bibliographic record
Abstract
Introduction: Since the outbreak of the COVID-19 pandemic, an increase in racial discrimination and xenophobia directed towards Asians has been documented in Western countries. The consequences of the COVID-19 pandemic have also led to increases in mental health problems among people worldwide. Individuals from Asian backgrounds are at high risk for experiencing a dual-threat, due to risk for racial discrimination, in addition to general life and COVID-19-specific stressors. In Canada, the largest population of foreign- and Canadian-born immigrants are from Asian origins, while 74.9% of Canada’s international students in Canadian universities come from Asian countries. Considering the increase in incidents of racism and violence against Asian communities in Canada and the potential impacts of discriminatory events, our goal is to investigate associations between in-person and online racial discrimination and mental health among university students from Asian backgrounds, and the extent to which general coping strategies (e.g., problem-focused, emotion-focused, physical activity) contribute to better mental health outcomes among students. Because individuals from immigrant backgrounds, including Asian, are highly heterogeneous in terms of their immigration characteristics (e.g., immigrant status, length of residence), we will also examine the extent to which cultural processes (i.e., acculturation, cultural identity) affect associations between racial discrimination and mental health. Methods and analyses: University students from Asian backgrounds will be asked to complete an online survey examining mental health, in-person and online racial discrimination, physical activity, coping strategies, and cultural processes (i.e., acculturation, cultural identity). Hierarchical multiple regressions will be conducted to examine associations between racial discrimination and mental health, and the moderating role of coping strategies and cultural processes. Ethics and Dissemination: This project has received ethics approval from the University of Ottawa Research Ethics Board. Results of the study will be published in UOJM and can later be submitted for internal or external conference presentations or other journals, recognizing UOJM as the primary publisher.
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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 | 0.032 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.106 | 0.031 |
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".