Mental Health Factors Impacting Indigenous Students’ Academic Performance: Empirical Evidence from Canadian Universities
Bibliographic record
Abstract
Indigenous students in Canada experience both higher rates of mental health challenges and persistent educational disparities, yet whether mental health conditions differentially impact their academic performance remains empirically unexamined. Using survey data from 8,652 students across 16 Canadian universities in Spring 2022, we test whether the relationship between psychological distress and academic performance differs by Indigenous status. Our results show that mental health challenges are differentially associated with academic performance for Indigenous students. Specifically, the negative association between stress and GPA is 1.4 times stronger for Indigenous students compared to non-Indigenous peers; for anxiety, 1.6 times stronger; and for depression, 1.8 times stronger. These differential effects remain robust across alternative mental health measures including diagnostic indicators and 30-day stress severity. These findings reveal differential vulnerability beyond prevalence differences alone, suggesting that identical mental health symptoms are associated with substantially larger academic performance decrements for Indigenous students. This hidden multiplier of educational inequality has direct implications for university support systems, indicating that achieving equity requires differentiated interventions calibrated to both amplified academic consequences and Indigenous students' cultural strengths, rather than universal approaches developed for general populations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".