Untangling the Health Impacts of Mexico – U.S. Migration
Bibliographic record
Abstract
Research has found that immigrant health has a tendency to decline with time spent in the United States. Using data from the Mexican Migration Project from 2007-2014, this paper is the first to test the impact of domestic and international migration on different types of health measures. Results find cumulative U.S. migration experience has a negative impact both on self-reported and objective health measures. By contrast, the number of trips to the United States and migrations made within Mexico impact individual’s self-assessment of their health but not objective health measures. The analyses suggest that differences in self-reported versus objective health measures may help to explain mixed results in the literature. Results suggest that individual’s health will suffer considerably more from U.S. migrations than from migration within Mexico which is consistent with the acculturation hypothesis. Not surprisingly, high levels of BMI and smoking are significant predictors of negative self-reported and objective health. There is also a troubling significant negative trend in health over time observed in the sample. Taken as a whole, these results suggest that even short trips to the United States can have a negative health effect on immigrants if they are repeated.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".