Development of emergency medicine and critical care masters program for nurses at Addis Ababa University, School of Medicine.
Bibliographic record
Abstract
BACKGROUND: In Ethiopia, though all health care facilities have rooms available for ill and injured patients, emergency care has always remained suboptimal. Poor organization, lack of properly trained staff and lack of timely identification of the critically sick are the reasons. The role of nurses in the emergency rooms is very vital to im- prove patient survival. To address this pressing health care need and improve the emergency rooms (ER) nursing care, Addis Ababa University School of Medicine (AAU-SM) prioritized Emergency Medicine and Critical Care Nursing Training Program. The initial training began in September 2010 with a class of 20 students. Of these, 18 nurses successfully completed the Emergency Medicine and Critical Care Masters of Nursing program and graduated in 2012. OBJECTIVES: To review the Emergency medicine and Critical Care Masters training program for nurses developed and implemented at AAU-SM in partnership with the University of Wisconsin (UW) and the University of Toronto (UT) and to evaluate the progress and challenges to date. METHODS: An Emergency Medicine Task Force (EMTF) organized at AAU-SM developed a two years modular type of EM and Critical Care masters program curriculum for nurses that is co-implemented by faculty teachers from AAU-SM, UT and UW. In this article both the curriculum and other relevant materials are used as a resource. RESULTS: Thirty eight nurses have already graduated with Masters in Emergency Medicine and Critical Care. Equal number of trainees are currently in full-time training. Their skill and competency log book is going according to the curriculum expectation. CONCLUSION: This EM and Critical Care masters training program for nurses is successfully implemented. This program has also shown that the number and qualification of trained personnel capacity in low resource setting health care system can be effectively improved by partnership with developed training institutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".