Development of a virtual peritoneal dialysis resource for registered nurses
Bibliographic record
Abstract
Background: Peritoneal dialysis (PD) is a common renal replacement therapy for patients with \nend-stage renal disease (ESRD) and chronic kidney disease (CKD). Poor care of patients who \nuse PD could lead to severe complications, including peritonitis, exit-site infection, technique \nfailure and death. To ensure safe and effective peritoneal dialysis, nurses must receive \ncomprehensive education and training on the proper techniques and procedures involved in the \nprocess. This will help them develop the skills and expertise to perform PD tasks proficiently and \nprovide the best care to patients undergoing dialysis treatment. Purpose: To assess current \nevidence on virtual education methods to develop an educational resource for registered nurses \nproviding care to patients who use peritoneal dialysis. Methods: Three methods were used to \ncollect information for this project. First, an integrative literature review was conducted using a \nliterature search in CINAHL, PubMed, and Scopus. Eleven relevant articles were identified, and \nresearch studies were critically appraised using standardized appraisal tools. Second, \nconsultations with stakeholders (i.e., registered nurses, physicians, managers, patient care \nfacilitators, and vendor representatives) were conducted to determine the learning needs of \nregistered nurses in Newfoundland and Labrador (NL) and gather recommendations and \nfeedback from all stakeholders regarding the development of a virtual peritoneal dialysis \nresource. Finally, an environmental scan was conducted to determine current resources and the \nbest options for registered nurses. Findings: Registered nurses were effectively educated on \nperitoneal dialysis through virtual education methods, which included computer-assisted \nprograms, e-modules, and Microsoft Teams. These findings aligned with the outcomes of the \nconsultations and environmental scan. Conclusion: Limited literature exists on virtual methods \nto educate nurses about peritoneal dialysis. However, available research confirms the \neffectiveness of online methods for this purpose. An evidence-based virtual peritoneal dialysis \nresource has been developed to aid nurses in caring for patients who use peritoneal dialysis. This \nvirtual resource will be shared with Newfoundland and Labrador Health Services (NLHS) \nmembers so they can plan to implement it.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".