Perioperative Nursing Training in Rwanda in Partnership with American Universities: The Journey So Far
Bibliographic record
Abstract
Nurses within a surgical team play such a fundamental role in the success of a surgery that they require specific training for the purpose. However, in Rwanda, there has been a severe scarcity of perioperative nurses. This article describes the collaborative effort for perioperative nursing training by the University of Rwanda (UR) and the Ministry of Health (MOH) with the Human Resources for Health (HRH) Program and a consortium of American Universities. The goal of the HRH program has been to build up the capacities of health professionals both in academia and clinical settings so as to address the shortage of qualified staff. In that regard, the UR in 2015 started a Masters program in nursing in eight specialties, of which one was perioperative nursing. The aim of this paper is to highlight the training process, success, and challenges of perioperative nursing training in Rwanda. The training has so far been successful, with the 19 nurses who completed the program working now in academic and clinical teaching institutions. Students in the program have also increased their number of research publications in peer-reviewed journals and international conference presentations. The UR and its partners are investing in the sustainability and excellence of this program. Using the import-of-experts approach to train Rwandans within their country, the program addresses the scarcity of specialists in various disciplines within the nursing profession. As a consequence, countries where the lack of specialized nurses poses challenges may adopt this partnership strategy.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".