Nurses’ and nurse educators’ experiences of a Pediatric Nursing Continuing Professional Development program in Rwanda
Bibliographic record
Abstract
OBJECTIVES: In 2016, a Pediatric Nursing Continuing Professional Development (PNCPD) program was created and implemented in Kigali, Rwanda, through the Training, Support, and Access Model (TSAM) for Maternal, Newborn, and Child Health (MNCH). This partnership project between Canada and Rwanda provided pediatric nursing education to forty-one Rwandan nurses and nurse educators in 2018 and 2019. The objective of this research study was to explore the experiences of nurses and nurse educators applying pediatric knowledge and skills to academic and clinical settings after participating in the PNCPD program. METHODS: This study was situated within an interpretive descriptive perspective to explore the ways in which knowledge gained during the PNCPD program in Rwanda was applied by nurses and nurse educators in their nursing practice, both academically and clinically. Data was collected through individual interviews. Inductive content analysis was used for data analysis. RESULTS: The analysis of the interviews resulted in the emergence of five themes: Transformations in Pediatric Nursing Practice, Knowledge Sharing, Relationship-Based Nursing, Barriers and Facilitators to Knowledge Implementation, and Scaling-up PNCPD within the Health System. CONCLUSIONS: The results of this study have the potential to inform positive changes to child health care in Rwanda, including scaling up pediatric nursing education to other areas of the healthcare system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".