Exploring Loneliness, Social Support and Adaptability of International Students in Canada during COVID-19
Bibliographic record
Abstract
Background: The global COVID-19 pandemic has had extraordinary adverse effects, negatively impacting people’s physical health, mental health, and well-being. Measures such as quarantine, lockdown, and social distancing have exacerbated social isolation, loneliness, and mental health challenges. International students, as a particularly vulnerable population, confronted numerous challenges, including a lack of social support and networks. They required considerable adaptability to cope with the changes and uncertainties brought about by the pandemic. This study explored the effects of loneliness, and social support on the adaptability of international students in Canada during the pandemic. Specifically, it aimed to: a) Examine the relationships among loneliness, social support, and adaptability. b) Investigate the potential moderating effect of social support on the relationship between feelings of loneliness and adaptability. Methods: We recruited 186 international students attending universities in Canada to complete the informed consent and an online survey during the pandemic COVID-19. Participants took approximately 40 minutes; a $10 Amazon gift card was offered to the participants as an appreciation. Participants were measured on the UCLA Loneliness Scale, Perceived Social Support Scale, Adaptability Scale, and demographic questions. The data analysis was conducted in IBM SPSS 26. Results: During the pandemic COVID-19, international students in Canada demonstrated that better adaptability was significantly associated with lower levels of loneliness and greater social support. Feelings of loneliness were found to negatively impact predicted adaptability; however, the effect was fully moderated by the presence of social support. Additionally, the findings highlighted gender differences in how international students adapted to the challenges of the pandemic. Discussion and conclusion: Our discussion focuses on practical suggestions that can help international students enhance their adaptability and build stronger social support networks, ultimately reducing feelings of loneliness while studying abroad in Canada. Our conclusions emphasize the importance of enhancing adaptability among international university students to reduce feelings of loneliness during the pandemic COVID-19 in Canada. We also recommend prioritizing social support as a protective factor, which plays a crucial role in mediating the effects of loneliness on adaptability.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".