Enhancing the education of paediatric nurses: A positive step towards achieving sustainable development goals
Bibliographic record
Abstract
AIM: The aim of this discursive paper was to describe and expound on how paediatric nurses will be able to address the needs of children and adolescents through the lens of selected Sustainable Development Goals (SDGs) in Rwanda. DESIGN: A discursive analysis of SDGs relating to the roles of paediatric nurses in the context of Rwanda. METHODS: A discursive method using SDGs as a guiding framework is used in this paper. We drew on our own experiences and supported them with the available literature. RESULTS: A collection of contextually relevant examples of how paediatric nurses will be able to address the needs of children and adolescents through the lens of selected SDGs in Rwanda was discussed. The selected SDGs expounded on were: no poverty, good health and well-being, quality of education, decent work and economic growth, reduced inequalities, and partnerships for the goals. CONCLUSIONS: There is no doubt that the paediatric nurses in Rwanda play undeniable key roles in attaining SDGs and their targets. Thus, there is a need to train more paediatric nurses with the support of the interdisciplinary partners. Collaboratively, this is possible in the bid to ensure equitable and accessible care to the current and future generations. PUBLIC CONTRIBUTION: This discursive paper is intended to inform the different stakeholders in nursing practice, research, education and policy to support and invest in the advanced education of paediatric nurses for attainment of the SDGs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| 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".