The Impact of the Housing Crisis on International Students in Ontario from 2020- present
Bibliographic record
Abstract
This study examined the impact of the housing crisis on the mental health and academic performance of international students during the post-COVID-19 era. The research identified the challenges international students face in securing safe, affordable housing and how these challenges affect their overall well-being and academic success. The primary research question explored the extent to which housing insecurity impacts international students' mental health and academic performance. A survey was distributed to 100 international students, with 49 responses collected. Quantitative and qualitative data from the survey were analyzed to identify key trends and themes. The findings revealed that housing insecurity significantly affects mental health, leading to increased stress, anxiety, and depression. These mental health challenges, in turn, negatively impact students’ academic focus and performance. The study also highlighted the role of systemic barriers, such as high rent costs and limited housing options, in exacerbating the housing crisis. The study underscored the need for policy interventions and institutional support to address housing challenges faced by international students. Recommendations include expanding housing assistance programs, improving tenant protections, and providing mental health resources. These findings are relevant to policymakers, educators, and community organizations working to support international students’ success.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".