Pediatric pain management competencies taught to nursing students in Rwanda: Perspectives of nurse educators, preceptors and nursing students
Bibliographic record
Abstract
Competency for pediatric pain management is fundamental for nurses’ responsibility in caring for pediatric patients with pain. However, effective nursing management of pain in hospitalized children continues to be a challenge more often linked to competency deficit as a consequence of unpreparedness in the pre-licensing education. Previous studies have established that nursing students exhibited lack of knowledge and poor attitudes regarding pediatric pain management, but none of the studies were done in the Rwandan context. The current study explores the pediatric pain management competencies taught to nursing students in Rwanda. An exploratory descriptive qualitative design based on face-to-face individual interviews and focus group discussions was utilized. Fourteen nurse educators and preceptors and nineteen nursing students were recruited from five study settings to explore their perspectives about pediatric pain management competencies taught to nursing students. Participants’ narratives were analysed using thematic analysis from which six main themes emerged. Participants narrated that competencies related to children pain assessment, pain medication and non-drug pain management interventions were taught to students. However, findings also revealed the challenges that impacted the teaching and learning of paediatric pain management, which need to be addressed for the improvement of pre-service training about pain management in children. The findings from the study suggested further research for a better understanding of the nature of those challenges to inform tailored strategies aimed at improving quality health care provision to children through an improved pediatric pain management education at the undergraduate level.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".