A Syllabus for Teaching Peritoneal Dialysis to Patients and Caregivers
Bibliographic record
Abstract
Being aware of controversies and lack of evidence in peritoneal dialysis (PD) training, the Nursing Liaison Committee of the International Society for Peritoneal Dialysis (ISPD) has undertaken a review of PD training programs around the world in order to develop a syllabus for PD training. This syllabus has been developed to help PD nurses train patients and caregivers based on a consensus of training program reviews, utilizing current theories and principles of adult education. It is designed as a 5-day program of about 3 hours per day, but both duration and content may be adjusted based on the learner. After completion of our proposed PD training syllabus, the PD nurse will have provided education to a patient and/or caregiver such that the patient/caregiver has the required knowledge, skills and abilities to perform PD at home safely and effectively. The course may also be modified to move some topics to additional training times in the early weeks after the initial sessions. Extra time may be needed to introduce other concepts, such as the renal diet or healthy lifestyle, or to arrange meetings with other healthcare professionals. The syllabus includes a checklist for PD patient assessment and another for PD training. Further research will be needed to evaluate the effect of training using this syllabus, based on patient and nurse satisfaction as well as on infection rates and longevity of PD as a treatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".