Mental Health and Wellbeing Among International Students in Canada
Bibliographic record
Abstract
International students contribute significantly to Canada’s multicultural fabric and economy, with over 800,000 enrolled in Canadian educational institutions as of recent years. While they gain access to quality education and diverse opportunities, the experience comes with challenges that profoundly affect their mental health. This study explores the psychological and emotional well-being of international students in Canada, focusing on factors such as cultural adjustment, academic pressure, financial constraints, and social isolation. We conducted qualitative research and carried out 10 online and in person interviews. The key findings reveal that international students often face cultural shock, navigating unfamiliar norms, languages, and societal expectations. Academic pressure is intensified by adapting to new teaching methodologies, maintaining high performance, and meeting visa requirements. Financial challenges—ranging from high tuition fees to limited work opportunities— further contribute to stress. Moreover, the lack of a robust support network and feelings of isolation exacerbate loneliness and anxiety. The mental health impacts include an increased prevalence of stress, depression, anxiety disorders, and, in some cases, burnout. Unfortunately, barriers to accessing mental health services—such as stigma, language challenges, and insufficient culturally sensitive support—often leave these issues unaddressed. The study highlights the urgent need for tailored mental health interventions, including on-campus counselling services, peer support groups, and community engagement initiatives that address the unique challenges of international students. By fostering inclusive environments and improving access to mental health resources, Canadian institutions can better support the well-being of their international student population, ensuring a more fulfilling and productive academic experience.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".