Country of birth and socioeconomic disparities in utilisation of health care and disability pensions - a multilevel approach
Bibliographic record
Abstract
Besides individual characteristics, people born in the same country may present a related pattern of health status and health care utilisation, perhaps because they share a number of socioeconomic and cultural characteristics in addition to their common geographic origin and language. Rather than using simple ethnical or geographical categories, we apply multilevel regression analysis with individuals nested within countries of birth. By this innovative approach the present thesis investigates socioeconomic differences in health care utilisation and disability pensions in the city of Malmö, Sweden, and the role country of birth plays in this context. It is based on the Register for Resource Allocation (1999 and 2003). Independently of individual socioeconomic characteristics, this thesis identifies a contextual phenomenon related to country of birth that conditions individual health care utilisation and receiving a disability pension. Among other findings we observed that men of low income and those from countries with low economies showed greater total health care utilisation than those with high incomes or who were born in countries with high incomes. However, those individuals presented a lower health care utilisation of private health care providers. Low educational achievement and living alone were associated with a higher likelihood of receiving a disability pension. Individuals from middle income countries also had a greater chance of receiving a disability pension. Interestingly, country of birth modifies individual level socioeconomic associations. The country of one's birth appears to play a significant role in understanding how individual socioeconomic differences bear on the likelihood of utilising health care services and of receiving a disability pension.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".