Utilisation of healthcare by immigrant adults relative to the host population: Evidence from Ireland
Bibliographic record
Abstract
While there is a broad consensus that barriers to access in the utilisation of healthcare exist for immigrants in the US, European evidence exploring this issue paints a mixed picture, with studies from a variety of European jurisdictions presenting different conclusions. In this context, Ireland, a European country with substantial private involvement in healthcare delivery, and, a largely young immigrant population, provides an opportunity to investigate the healthcare utilisation of immigrants compared to natives in a European country with mixed private-public healthcare provision. The healthcare utilisation patterns of immigrants (defined as residents with a foreign country of birth) and native-born participants were analysed from a nationally representative health survey of 6,326 adults, carried out in Ireland in 2016. An array of socio-economic and health information was collected such that regression analysis on healthcare consultations accounted for confounding factors. Non-native residents of Ireland born outside the UK were less likely to have attended a General Practitioner (Odds ratio (OR): 0.62 [95% Confidence Interval (CI): 0.51–0.74]; p<0.001) or consultant doctor (OR: 0.60 [95% CI: 0.47–0.76]; p<0.001) in the previous year, relative to Irish-born individuals. UK-born residents of Ireland displayed similar utilisation patterns to those of the native population in terms of GP visitation, but a higher likelihood of having attended a consultant (OR: 1.44 [95% CI: 1.14–1.816]; p = 0.004). Lower use of healthcare by those born outside Ireland and the UK relative to the native Irish population may be due to different approaches to healthcare utilisation or obstacles to healthcare utilisation. The findings suggest that the utilisation of healthcare by immigrants merits continued policy attention to respond to the needs of these key groups in society and facilitate integration.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".