Ethnicity And Depression Among Maritime University Students In Canada
Bibliographic record
Abstract
Introduction Depression is among the most common mental illnesses in Canada. Although many factors contribute to depression, stress is among the most commonly reported. Studies suggest that marginalized groups often experience high levels of stress. Objective To examine associations between ethnicity and depressive symptoms among university students. Aim To identify if ethnic groups, particularly Aboriginal students, are at greater risk of depression. Methods Online survey data were collected from students attending eight universities in the Canadian Maritime Provinces (n = 10,180). Depressive symptoms were assessed using the 12-item version of the Center for Epidemiological Studies Depression Scale. Ethnicity was organized into five groups: Caucasian only, Aboriginal only, Aboriginals with other ethnicities, Mixed Ethnicity (not including Aboriginal), and Other (single ethnicity not including Aboriginal or Caucasian). Unadjusted and adjusted logistic regression models were used to assess associations between ethnicity and elevated depressive symptoms. Adjusted models accounted for demographic, socioeconomic, and behavioural characteristics. Results In adjusted analyses for men, Mixed (OR: 2.01; 95% CI: 1.12–3.63) and Other ethnic students (OR: 1.47; 95% CI: 1.11–1.96) were more likely to have elevated depressive symptoms than Caucasians. There were no differences between those who were Aboriginal and those who were Caucasian. In unadjusted and adjusted analyses for women, depressive symptoms in ethnic groups (including Aboriginals) were not significantly different from Caucasians. Conclusion Among male university students in the Maritime, ethnicity (other than being Aboriginal) was associated with depressive symptoms in comparison to Caucasians, after adjusting for covariates. However, among women, ethnicity was not significantly associated with depressive symptoms. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".