The mental health of Korean transnational mothers: A scoping review
Bibliographic record
Abstract
BACKGROUND: A recent migration trend from Korea is transnational family arrangement where mothers migrate with children to English-speaking countries, while the fathers stay in the home country. Mothers in these families may experience more challenges than other family members because they have to adjust to a new country, new parenting role and family separation. But little is known about their mental health. AIMS: This article scopes the evidences in the literature on impact of transnational family arrangement and migration on the mental health of Korean transnational mothers. METHOD: A comprehensive search was undertaken in 16 databases and 17 studies were identified. RESULTS: The evidence on the mental health of Korean transnational mothers was analyzed into two themes: (1) challenges and life difficulties, (2) psychological and emotional states. In relation to the life difficulties such as role changes, adaptation in the host country and lack of social support, the mothers reported anxiety, depression, increased psychological distress and feeling of isolation. Positive perceptions such as sense of empowerment and increased self-confidence were also reported. CONCLUSION: The evidence suggests that there may be a potential for vulnerability to mental health problems in Korean transnational mothers. More research is needed to assess their mental health and to identify the risk factors.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".