Immigration, employment relations, and health: Developing a research agenda
Bibliographic record
Abstract
BACKGROUND: International migration has emerged as a global issue that has transformed the lives of hundreds of millions of persons. Migrant workers contribute to the economic growth of high-income countries often serving as the labour force performing dangerous, dirty and degrading work that nationals are reluctant to perform. METHODS: Critical examination of the scientific and "grey" literatures on immigration, employment relations and health. RESULTS: Both lay and scientific literatures indicate that public health researchers should be concerned about the health consequences of migration processes. Migrant workers are more represented in dangerous industries and in hazardous jobs, occupations and tasks. They are often hired as labourers in precarious jobs with poverty wages and experience more serious abuse and exploitation at the workplace. Also, analyses document migrant workers' problems of social exclusion, lack of health and safety training, fear of reprisals for demanding better working conditions, linguistic and cultural barriers that minimize the effectiveness of training, incomplete OHS surveillance of foreign workers and difficulty accessing care and compensation when injured. Therefore migrant status can be an important source of occupational health inequalities. CONCLUSIONS: Available evidence shows that the employment conditions and associated work organization of most migrant workers are dangerous to their health. The overall impact of immigration on population health, however, still is poorly understood and many mechanisms, pathways and overall health impact are poorly documented. Current limitations highlight the need to engage in explicit analytical, intervention and policy research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".