Exclusion and racial trauma: mental health costs for Canadian university students of color
Bibliographic record
Abstract
Exclusion in academic settings is a pervasive issue that profoundly impacts marginalized students, particularly BIPOC (Black, Indigenous, and People of Color) individuals. This study examines the nature of exclusion, focusing on the role of racial microaggressions as operational mechanisms that undermine mental health and academic success. Participants completed an online survey that included measures of depression, racial trauma, racial microaggressions, and affect. Results demonstrate strong correlations between frequent microaggressions and heightened symptoms of racial trauma and depression. Despite prior evidence suggesting a protective role, ethnic identity did not buffer these adverse outcomes, with higher ethnic identity sometimes exacerbating depressive symptoms. Our findings also highlight systemic exclusion in institutional structures, such as ethnocentric curricula, inequitable policies, and lack of diverse representation in leadership. These systemic barriers compound interpersonal exclusions, creating environments where BIPOC students experience isolation, invalidation, and diminished belonging. Physiologically, perceived exclusion and discrimination activate chronic stress responses, contributing to health disparities through mechanisms such as epigenetic changes. While systemic racism contributes to these patterns, this study underscores the urgency of institutional reform to promote fairness and inclusivity. Efforts to address exclusion must extend beyond interpersonal interactions to structural changes in curricula, policy, and representation. These findings enhance our understanding of exclusion’s psychological and impacts, suggesting pathways for targeted interventions that promote belonging, mental health, and academic equity for marginalized students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".