Mental Health and Related Factors among Migrants from Myanmar in Thailand
Bibliographic record
Abstract
Purpose: To determine the prevalence of mental health problems in migrant workers from Myanmar in Thailand and to examine the relationship among factors that influence such problems. Lazarus’s transactional model of stress and coping was used to frame the study. Design: A cross-sectional design, involving interviews with 445 migrant workers from Myanmar, was used to collect data. Data collection tools included a sociodemographic form, the Interpersonal Support Evaluation List (ISEL-12), the Acculturative Stress Scale (ASS), the Perceived Stress Scale (PSS) and the Hopkins Symptom Check List (HSCL-25). Findings: 11.9% of the participants reported symptoms of depression and/or anxiety. Gender, self-rated physical health, and perceived general stress explained 49.0% of the probable presence of mental health problems and correctly classified 91.9% of cases. The low prevalence of these problems compared to other studies may be related to increased workplace and community stability. Conclusion: The findings may help health care professionals to understand how overall good health and community and workplace environments can support mental health and wellbeing for migrant workers. Health promotion strategies have the potential to be an important future focus for health care professionals who provide services to this population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".