Immigration and self-reported health status by social class and gender: the importance of material deprivation, work organisation and household labour
Bibliographic record
Abstract
OBJECTIVE: Spain and Catalonia have experienced several immigration waves over the last century. The goal of this study was to examine the role of social class and its mediating pathways (ie, work organisation, material deprivation at home and household labour) in the association between migration status and health, as well as whether these associations were modified by social class or gender. SETTING: Barcelona city, Spain. DESIGN AND PARTICIPANTS: The study used the Barcelona Health Interview Survey, a cross-sectional survey of 10,000 residents of the city's non-institutionalised population in 2000. The present study was conducted on the working population, aged 16-64 years (2342 men and 1872 women). The dependent variable was self-reported health status. The main independent variable was migration status. Other variables were: social class (measured using Erik Olin Wright's indicators); age; psychosocial and physical working conditions; job insecurity; type of labour contract; number of hours worked per week; material deprivation at home and household labour. Two hierarchical logistic regression models were built by adding different independent variables. RESULTS: Among men, foreigners presented the poorest health status (fully adjusted odds ratios (OR) 2.16; 95% CI 1.14 to 4.10), whereas among women the poorest health status corresponded to those born in other regions of Spain. There was an interaction between migration and social class among women, with women owners, managers, supervisors or professionals born in other regions of Spain reporting a worse health status than the remaining groups (fully adjusted OR 3.60; 95% CI 1.83 to 7.07). CONCLUSION: This study has shown that the pattern of perceived health status among immigrant populations varies according to gender and social class. These results have to be taken into account when developing policies addressed at the immigrant population.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".