Introducing Physician Assistants into the Irish Healthcare System. An Integrated Clinical Workforce Reconfiguration Strategy
Bibliographic record
Abstract
The Irish health system is facing a unique and unprecedented workforce challenge with acute shortage of Non-Consultant Hospital Doctors (NCHDs) threatening to undermine the overall health service delivery system. Ireland‟s requirement to comply with the European Working Time Directive (EWTD) aimed at regulating the working hours of NCHDs, lack of sufficient funding due to economic recession, changes in immigration rules, absence of structured training programmes for most junior doctors and demographic changes are some of the prevailing circumstances that has given rise to NCHD shortage in the rapidly evolving Irish health system. Using the Health Service Executive (HSE) Change Model, this project presents a strategy for increasing the capacity and quality of the mid-level clinical workforce by introducing Physician Assistants into the system. Physician Assistants (PAs) are clinicians who are academically qualified to provide medical and surgical services to patients in a range of settings under supervision of doctors. While recognising the uniqueness of the Irish culture and the need for additional policy changes to create a sustainable health system, with the required skill mix and flexibility, this paper presents part of a potential solution to this workforce challenge. The implementation phase of this project is still on-going at the time of this publication. However, the English, Scottish, Canadian, Australian and American health systems have been used as bench marks for preliminary assessment of this project. The result is a clear indication that this system-wide change will rapidly evolve to create a more flexible, integrated and sustainable workforce for the future of the Irish health system.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".