Mental Health and Cross-Cultural Adaptation of Chinese International College Students in a Thai University
Bibliographic record
Abstract
Thai Immigration Department shows the total number of Chinese nationals residing in Thailand at 91,272 in 2015, however, academic studies reveal the figure to be as high as 350,000-400,000 in the past decade. In terms of the huge population, except economic benefit to Thailand and more cross-cultural settings in the campus, there is a critical issue requiring urgent attention. Colleges cannot guarantee high-quality learning and consequently cannot attain their mission, accomplish their goals, or serve their valuable social, economic and public objectives without engaging in the mental and behavioral health matters of their students. Accordingly, this study aimed to examine Chinese international students’ mental health and cross-cultural adaptation to study abroad in a university at Bangkok and investigate whether or not two factors were related to one another. A survey was applied for this investigation. The participants were 900 Chinese international students at a Thai university. The research discovered that different levels of college degrees and length of residence in Thailand were two main factors to influence mental health and cross-cultural adaptation. Incoming students and graduates specifically has a potential problem in cross-cultural adaptation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".