Development of an educational resource for dialysis nurses about pediatric hemodialysis
Bibliographic record
Abstract
Background: Hemodialysis (HD) is a form of renal replacement therapy used for the treatment of kidney disease. In Newfoundland and Labrador (NL), there is a high incidence rate of HD use among the adult population compared to other Canadian provinces; however, the incidence of HD use in the pediatric population is historically rare. The recent rise in multi-organ inflammatory syndrome in children (MIS-C) secondary to COVID-19, combined with the unpredictable weather preventing patient transfer to larger pediatric hospitals elsewhere in Canada, has led to an increased requirement for pediatric HD use in NL. There are no policies or educational resources for nurses specific to pediatric HD in NL. Purpose: To develop an educational resource for nurses that will improve their knowledge and skills to perform HD on pediatric patients in NL and, ultimately, improve patient and family outcomes. Methods: I completed an integrative literature review, followed by consultations with local field experts including eight nurses, the clinical educator of the Dialysis Program at Eastern Health, and two nephrologists. I completed an environmental scan with the HD Program Coordinator at a pediatric hospital in Ontario. Based on the information obtained, I developed an education module. Results: Through literature review, consultations, and environmental scan, I determined the content and delivery method(s) for the educational resource. The content included the lived experience of the patient/family and nurse, nursing care of a pediatric patient and family, vascular access with infection prevention, fluid and volume removal, vital sign ranges, dialysis prescription, machine set-up and programming, and nursing assessment. The most frequently cited education delivery methods in the literature were lecture, simulation, hard-copy, and online module. The most frequently identified delivery methods through the consultations and environmental scan were online module, simulation, and hard-copy. As such, I chose an online module and a hard copy. Conclusion: The online module will be available on LEARN, the Eastern Health online education platform, once approved. A hard copy will be available in the dialysis unit at the Health Sciences Centre in Eastern Health. The educational resource with all components will aid in improving nurses’ competency to perform pediatric HD, thereby improving patient and family outcomes.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".