Introducing the physician associate role in Ireland: Evaluation of a hospital based pilot project
Bibliographic record
Abstract
Objective: Ireland has medical workforce challenges and a growing demand for services. One strategy is to include Physician Associates (PAs) in healthcare settings. A pilot study was undertaken with PAs recruited from North America and the United Kingdom to work in a large Dublin teaching hospital.Methods: Four PAs were deployed on surgical services. Communication with the hospital staff preceded their employment. A series of interviews were undertaken at the beginning [2015] and end [2017] of the project. Data collection included surveys and interviews with staff and PAs.Results: Despite a series of communications about the employment of PAs a lack of awareness among hospital staff prevailed. This presented a challenge for the PAs to assume their role and for staff to bring them on board. Once on board those staff who worked with the PAs found their role beneficial in terms of continuity of care and skillset. Recommendations for inclusion of PAs in any new employment should include a more robust stakeholder engagement and promulgation throughout the wider healthcare system.Conclusion: Attitudes about the adoption of the PA have come slowly when first introduced in a country and Ireland seems no exception. At the same time communication lessons were learned about introducing a new health provider role in Irish society.
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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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".