A study of nurse‐based Injury Units in Ireland: An emergency care development for consideration worldwide
Bibliographic record
Abstract
The aim of this 2018 research study was to determine why nurse-based Injury Units were developed in Ireland and how they function in the Irish healthcare system, including what they contribute in relation to addressing the healthcare needs of Irish citizens. A document review was completed and interviews of nurse practitioners and physicians working in Irish Emergency Rooms (ERs) and Injury Units, as well as nurse managers with responsibility for Injury Units and health service executives who helped design Injury Units. A new model of emergency care was needed 20 years ago when two issues were apparent. The first was concern over unsafe care in small ERs as a result of low patient volumes and staff not having ER expertise. The second issue was long waits for ER care. Considerable opportunity for change was present, including financial imperatives and nurse, physician, and political leaders who were together ready to design and move a new-to-Ireland ER services model and nurse practitioner education forward. The Injury Unit model is based on nurse practitioners providing a defined set of services to nonurgent patients in daytime hours. This model was pilot tested and is being implemented across Ireland after it was determined that quality services were being rapidly and safely provided. Nurse practitioner education was also initiated and is now in expansion mode to gain 700 more nurse practitioners by the year 2021 over the current 240.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".