A source and a destination country for migrant healthcare workers: the case of Ireland
Bibliographic record
Abstract
Abstract Background Ireland has a long tradition of emigration, particularly to countries such as the United Kingdom and the USA. Since 2000, Ireland has become increasingly reliant upon migrant healthcare workers. At present, 43% of nurses and 45% of physicians registered in Ireland, trained internationally. Methods This study draws on literature and available data (migration and registration data) to illustrate recent patterns of healthcare worker migration into and out of Ireland. Results The data show that Ireland depends heavily on migrant healthcare workers to staff its health system. In 2021-22, internationally trained healthcare workers comprised 71% of new entrants to the medical register and 69% of new entrants to the nursing register. In terms of outward migration, the data also indicate a consistent pattern of outward migration of physicians to countries such as Australia, the United Kingdom, Canada, and New Zealand. Conclusions A heavy (and growing) reliance on migrant healthcare workers to staff the Irish health system implies a failure to train or retain sufficient healthcare workers locally and indicates weak healthcare workforce planning capacity. It also highlights a disconnect between Irish healthcare workforce planning and the WHO global code on the international recruitment of health personnel. This study will consider some of the risks associated with high rates of inward and outward healthcare worker migration in the Irish context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".