Experience of adapting and implementing an evidence-based nursing guideline for prevention of diaper dermatitis in a paediatric oncology setting
Bibliographic record
Abstract
BACKGROUND: Diaper dermatitis is one of the most common skin problems in children often caused by irritants that promote skin breakdown, such as moisture and faecal enzymes. It has been estimated that the incidence of diaper dermatitis is as high as 50% in children receiving chemotherapy. The scientific literature suggests a variety of preventative measures, but only a minority are systematically tested and supported by clinical evidence. AIM: The purpose of this paper is to adapt and implement a skincare guideline to better prevent diaper dermatitis in the paediatric oncology population. METHODS: The Knowledge to Action process was used to guide the adaptation and implementation of the new guideline. As part of this process, different tools were used to identify and review selected knowledge (Appraisal of Guidelines Research Evaluation instrument), to tailor and adapt knowledge to the local context (ADAPTE process), to implement interventions (Registered Nurses' Association of Ontario toolkit) and to evaluate outcomes (qualitative analysis). The main outcomes measured included implementation of the guideline and nursing practice change. RESULTS: The guideline was successfully implemented as reported by nurses in focus group sessions and as measured by changes in nursing documentation. CONCLUSION: The implementation of the guideline was successful on the account of the interplay of three core elements: The level and nature of the evidence; the context in which the research was placed; the method in which the process was facilitated.
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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 | 0.040 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".