Facilitation of nursing students' competency acquisition for paediatric pain management in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
Objective: To elucidate evidence regarding nurse educators' and preceptors' capacity to facilitate students' learning about paediatric pain management (PPM) in low- and middle-income countries(LMICs).Methods: The five-stage framework by Arksey and O'Malley guided this review. Studies published in English between January 2010 and April 2020 were searched using EBSCO Host/ ScienceDirect, CINAHL, MEDLINE, PUBMED and Scopus. Of 300 papers identified through the search strategy 27 primary research articles were retained: quantitative (n=18), qualitative (n=8) and mixed-methods (n=1).Results: Knowledge deficiency and inappropriate attitudes toward PPM, lack of autonomy in decisionmaking, scarcity of resources and cultural misconception regarding pain in children were hindering the effective PPM in LMICs. Strategies including nursing curricula review, continuous in-service training, access to resources and the leadership support are required to optimise effective PPM and improve students' facilitation for learning about PPM.Conclusion: Further research is required as a body of evidence to support the development of a framework for capacity enhancement of nurse educators and nurse preceptors who facilitate nursing students acquiring competency for PPM in LMICs. Keywords: Pain management education, Children, Scoping review, Low- and middle- income countries
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".