GP emigration from Ireland: an analysis of data from key destination countries
Bibliographic record
Abstract
BACKGROUND: Ireland is experiencing a general practitioner (GP) workforce crisis, facing an ageing workforce, a growing population with increased life expectancy, and increased complexity of patients. The GP crisis threatens access to primary care in Ireland, as well as Ireland's aim to transform into a primary-care centred system of universal healthcare via the proposed "Sláintecare" healthcare reforms. The challenges faced are common to many countries as health systems seek to expand their medical workforce post-pandemic. In addition Ireland has a legacy of austerity policies which impacted the health system, and triggered/generated largescale doctor emigration. However, little is known specifically about GP emigration and the role it potentially plays in the GP workforce crisis. This paper aims to address the gap in knowledge about the level of GP emigration from Ireland and consider the implications for the Irish health system and health systems internationally. METHODS: As Ireland does not formally collect routine data on GP emigration, this paper presents routinely collected secondary data from four key destination countries; Australia, New Zealand, the United Kingdom, and Canada, in order to gain an initial picture of GP emigration from Ireland to these countries, from 2012-2021. The data were in the form of medical registration and immigration (visa) data and both stock (the total number of GPs registered in a country in a given year) and flow data (the number of GPs entering a country in a given year) were collated, where available. RESULTS: The stock data shows a substantial cohort of Irish-trained doctors working in general practice in key destination countries. The flow data suggests a relatively small annual emigration flow of GPs from Ireland to individual countries. However when compared with the total numbers of GPs trained in Ireland each year, the numbers are notable. CONCLUSIONS: The available data suggests a mixed picture regarding GP emigration from Ireland. There is a significant stock of Irish-trained GPs abroad which perhaps represents a potential cohort of GPs who could be encouraged to return to practice in Ireland as part of Ireland's strategy for addressing the GP workforce crisis. The annual flow of GPs from Ireland to key destination countries, while small, should be monitored and factored into GP workforce planning. As global demand for GPs increases, countries will inevitably compete with each other to attract and retain GPs (see for example Australia's recent move to attract and recruit Irish trained GPs). The paper highlights the need for improved routine data on the GP workforce in Ireland, including the need for a national GP workforce dataset, in order to ensure that national workforce planning efforts are informed by the latest evidence on GP emigration.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".