Labor migration in rural Nepal Arghakhanchi communities: impacts on left-behind caregivers and children
Bibliographic record
Abstract
BACKGROUND: Children from migrant families with absent parents are more likely to have poorer physical and mental health than children from non-migrant families. The impact of labor migration on left-behind family members in South Asian countries is not well-known. This study aimed to examine the patterns of labor migration and its impact on the health and development of children and their caregivers in rural Nepal. METHODS: Baseline family data collected from a school-based violence prevention program were utilized. Parents/caregivers (N = 346) with school-aged children (aged 3 to 15 years attending nursery to primary grades) from the rural Arghakhanchi district of Nepal were included in the study. A series of descriptive and chi-square analyses were carried out to explore the pattern of labor migration and differences between labor-migrant and non-labor-migrant families. Multivariate linear and logistic regression analyses were applied to explore the correlates and moderators involved. RESULTS: Labor migration has been a common practice in rural Nepal, with an estimated 49% of families having parents working overseas, mostly in India (57%) and Gulf countries (39%) on low-skill labor jobs. Labor migration was significantly associated with left-behind caregivers' and children's mental health. Left-behind caregivers in father-only labor-migrant families reported higher levels of depression than did parents in non-labor migrant families and left-behind children from labor-migrant families reported greater anger than did children from non-labor migrant families. The impact of labor migration on families was moderated by social class. For low social-class father migrant families, left-behind children were at greater risk for developmental delay and behavioral problems, but there seems to be a protective effect for high social-class father migrant families (with lower risk of developmental delay and problem behaviors compared to all other groups). CONCLUSIONS: Labor migration has a substantial impact on the mental health of left-behind families and children. The impact of labor migration may vary by living social-cultural context. Understanding the complex dynamics of labor migration has important implications for local and global migration-related health service planning.
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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) | 0.002 | 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".