Adjusting in a pandemic: Experiences of incoming international students
Bibliographic record
Abstract
Introduction Over 4 million students travel outside their home countries to pursue tertiary education in the world, with over 600,000 students traveling to Canada alone. Adjustment to new cultures has often been shown to be stressful. The COVID-19 pandemic has been a significant global event that has affected all aspects of life in different ways. Although there is research showing the negative impact of the COVID-19 pandemic on international students globally, the study of the experiences of incoming cohorts of international students, particularly during the process of planning, traveling, and arriving at the host country, is still evolving. Methods Given that international students are sometimes at higher risk for mental health concerns, this qualitative study sought to explore the experiences of six incoming international graduate students, ages 18 to 32, through a semi-structured interview, as they moved from their home country to Canada. It explored their cultural adjustment in the context of the COVID-19 pandemic using a thematic analysis, through a descriptive phenomenological paradigm. Results Six themes emerged: choosing graduate programs; influence of pandemic on admission acceptance, moving to Canada, university experiences, adjustment; and adjustment as a student. Overall, international students appear to experience a “double dose” of stressors - the typical stressors of student hood, COVID-19 related challenges, as well as their unique manifestations in the context of being an international student. Discussion Limitations and implications of the study are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".