The Effects of Psychosocial Factors on International Students’ Mental Health and Relevant Interventions
Bibliographic record
Abstract
Mental disorders are pervasive among global students enrolled in postsecondary education institutions, and the mental health conditions of international students (ISs) are seizing additional attention from professionals due to their unique challenges. This review aims to address three categories of psychosocial factors that are particularly affecting ISs’ mental health conditions, including 1) acculturative stress, 2) explicit and implicit discrimination and 3) interpersonal relationships/intercultural social efficacy. High mindfulness level, low perceived cultural distancing, high host language proficiency, and sufficient psychoeducation of educators and ISs are protective of ISs from acculturation-related distress. Low perceived host language proficiency, loneliness, and anxiety mediate the relationship between ISs’ mental health and discrimination. Possessing, maintaining, and enhancing existing healthy interpersonal relationships and social connectedness to the host culture provides ISs with social support to overcome psychological distress. Correspondingly, the article discussed interventions that have shown effectiveness and potential practical application value from both personal and institutional perspectives. In sum, mental health conditions are as noteworthy as academic competency among ISs, and it requires more investigations into the intervening process targeting psychosocial stressors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".