The prevalence of distress, depression, anxiety, and substance use issues among Indigenous post-secondary students in Canada
Bibliographic record
Abstract
This study aimed to estimate the prevalence of mental illness and substance use among Indigenous students attending Canadian post-secondary institutions. We obtained data from the National College Health Assessment - American College Health Association Spring 2013 survey, which includes 34,039 participants in 32 post-secondary institutions across Canada. We calculated prevalence estimates with 95% confidence intervals (CI). We compared Indigenous and non-Indigenous students using age- and sex-adjusted prevalence ratios (PR) obtained from Poisson regression models. Of the total sample, 1,110 (3.3%) post-secondary students self-identified as Indigenous. Within the past 12 months, Indigenous students had higher odds of intentionally injuring themselves (PR = 1.53, 95% CI = 1.27-1.84), seriously considering suicide (PR = 1.32, 95% CI = 1.12-1.56), attempting suicide (PR = 1.74, 95% CI = 1.16-2.62), or having been diagnosed with depression (PR = 1.26, 95% CI = 1.08-1.47) or anxiety (PR = 1.18, 95% CI = 1.02-1.35) when compared with non-Indigenous students. Indigenous students also had higher odds of having a lifetime diagnosis of depression (PR = 1.31, 95% CI = 1.17-1.47) when compared with non-Indigenous students. Indigenous students were more likely to report binging on alcohol (PR = 1.10, 95% CI = 1.02-1.19), using marijuana (PR = 1.21, 95% CI = 1.06-1.37), and using other recreational drugs (PR = 1.32, 95% CI = 1.06-1.63) compared to non-Indigenous students. This study demonstrates that Indigenous students at post-secondary institutions across Canada experience higher prevalence of mental health and related issues compared to the non-Indigenous student population. This information highlights the need to assess the utilization and ensure the appropriate provision of mental health and wellness resources to support Indigenous students attending post-secondary institutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".