The Healthy Immigrant Effect (HIE) in the UK.
Bibliographic record
Abstract
The so-called “Healthy Immigrant Effect” (HIE) is based on two complementary hypotheses: i) immigrants who recently arrived to a new country present a better health than the native-born population with similar socio-demographic characteristics; ii) immigrant´s health deteriorates faster than that of the native-born and converges towards native-born levels with the years lived in the host country. This phenomenon has been widely studied for working immigrants in countries who have traditionally received large flows of labour migration as Australia, Canada and the US. The aim of this paper is to study the possible existence of this HIE in the UK, a country which has recently experienced large figures of net labour immigration. For doing so, I use the United Kingdom Household Longitudinal Survey (UKHLS) 2009-2013. With this dataset I find that immigrants working in the UK, both females and males, report a better health status than their native counterparts, and that these differences cannot be entirely explained by observable characteristics. The only group of immigrants who does not show a significantly better self-reported health than that of the native-born population was immigrants born in Developing Asia. In addition, the health distribution of immigrants converges towards that of the native-born workers during the period of analysis. This was mainly led by a faster deterioration in the health of immigrants coming from developing countries, in particular females who recently arrived to the UK.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".