The impact of migration on the well-being of transnational families: new data from sending communities in Mexico
Bibliographic record
Abstract
We present results from a new study of the effects of migration to the USA on the well-being of transnational families in high emigration communities within Mexico. Our survey measured the well-being of family members in a variety of domains: economic, health, education, and child development for a representative sample drawn from high migration municipalities. Compared to those with no recent emigrants to the USA, Mexican households sending non-caregivers to the USA appear to gain economically without contributing to problems faced by children. However, when family caregivers migrate to the USA, the remaining members in Mexico struggle to meet the family's needs and children are more vulnerable to educational, emotional, and health problems. Children in households where a caregiver migrated were more likely to have frequent illnesses (10% vs. 3%, p<0.0001), chronic illness (7% vs. 3%, p=0.011), emotional problems (10% vs. 4%, p=0.006), and behavioral problems (17% vs. 10%, p=0.018) compared with children in households where the migrant was not a caregiver. Research, policy, and program implications of these findings are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".