The Impacts of Belongingness on The Well-Being of International Students in Canada During Covid-19: The Mediating Role of Perceived Stress and Acculturation Stress
Bibliographic record
Abstract
Background/Aim: The COVID-19 pandemic has had a significant negative impact globally, causing prolonged stress, isolation, and health and mental health challenges. International students in Canada were particularly vulnerable due to being far from home, facing language barriers, lacking support networks, and dealing with academic and financial stress, which led to increased mental health issues and reduced well-being for this group. This current study examined the effects of belongingness on well-being of international students in Canada during pandemic COVID-19. The aim of this quantitative study was to: (a) examine the relationships among belonging, perceived stress, acculturation stress, and well-being. (b) explore the potential moderating effect of perceived stress and acculturation stress on the relationship between feelings of belonging and well-being. Materials and Methods: 186 international students were recruited in the universities in Canada to complete the online informed consent form, and the online questionnaires, including the World Health Organization Five, the Satisfaction with Life Scale, the General Belongingness Scale, the UCLA Loneliness Scale, the Perceived Stress Scale, the Acculturative Stress Scale and the demographic information. Participants took about 40 minutes and were given either one bonus mark or $10 Amazon gift card. Results: Correlational analyses revealed that well-being was positively correlated with belonging, with acceptance belonging, and significantly negatively associated with rejection belonging, loneliness, perceived stress, and acculturation stress. Belonging and acceptance belonging positively predicted well-being. A mediational model indicated that perceived and acculturation stress mediate the link between belongingness and psychological well-being. Conclusions: The findings revealed that belonging is a positive predictor to well-being and highlight the need for prevention efforts to help international students reduce loneliness, perceived stress and acculturation stress, and increase acceptance belonging which could improve life satisfaction and well-being of international students in Canada during COVID-19.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".