Studying Away and Well-Being: A Comparison Study Between International and Home Students in the UK
Bibliographic record
Abstract
The aim of this study was to investigate the impact of being away from home on the well-being of international and domestic UK university students as a function of demographic factors, course load, support, personality, healthy lifestyle, and their employment of pre-planning and being at university strategies. A total of 510 students (n = 391 international and 117 British) completed an on-line survey to record demographic details and measure their well-being, quality of university life, and their being away from home strategies. The findings showed that International students reported greater quality of university life and used more pre-departure strategies; the female students reported a significantly more negative well-being and higher course demand than their male peers. A regression analysis showed that positive well-being was predicted by a positive personality, a healthy lifestyle, control and support for academic work, quality of university life and employing well-being strategies (using technology without over-reliance on it and the ability to unwind from study). Negative well-being, on the other hand, was predicted by a less positive personality and a less healthy lifestyle, a higher course demand, less control and support for academic work and less quality of university life. Moreover, the regression analysis showed that international students who employed more pre-departure strategies showed less negative well-being.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".