Integrated approach to perceived group discrimination and protective factors: Implications for well-being and academic outcomes among Asian university students in Canada
Bibliographic record
Abstract
Asian university students in North America faced intensified discrimination during the COVID-19 pandemic, impacting their well-being and academic outcomes. This study explored how group discrimination, when intertwined with protective factors including low internalized racism, social support, and resilience, relate to well-being and academic outcomes. Using Latent Profile Analysis (LPA), participants were grouped into four profiles: (1) low exposure protected, (2) high exposure vulnerable, (3) low exposure vulnerable, and (4) high exposure protected. Notably, the “(4) high exposure protected” profile characterized by high group discrimination but fortified with higher protective factors was significantly different from “(2) high exposure vulnerable” profile marked by high perceived group discrimination and weaker protective factors. We found that the (4) high exposure protected group, compared to (2) high exposure vulnerable group, exhibited significantly higher sense of belonging to the university community, significantly lower levels of depression and anxiety, as well as significantly higher levels of academic engagement. This result highlights that protective factors may alleviate the impact of group discrimination on well-being and academic outcomes. Implications for interventions aimed at supporting minority students’ welfare in educational settings are discussed, emphasizing the importance of enhancing protective factors to improve well-being and academic outcomes of minority students in the post-pandemic era.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".