Self-management support for peritoneal dialysis patients.
Bibliographic record
Abstract
UNLABELLED: The increasing prevalence of chronic illnesses and kidney disease, in particular, makes it necessary to adopt new approaches towards their management (Wagner, 1998). Evidence suggests that promoting self-management improves the health status of peritoneal dialysis (PD) patients, as they manage upwards of 90% of their own care. Patients who are unable to self-manage suffer from various complications. This project proposes an intervention aimed at improving self-management skills among PD patients. GOAL: To promote self-management in peritoneal dialysis patients. This is achieved through the following objectives: (a) develop an algorithm that can improve patients' ability to solve the specific problem of fluid balance maintenance, (b) develop an educational session for patients on how to use the algorithm, and (c) develop an implementation strategy in collaboration with the PD nurse. METHOD AND RESULTS: Three measures evaluate the effectiveness of the intervention. First, a telephone call log shows that participating patients call the clinic less to inquire about fluid balance maintenance. Next, a pre- and post-intervention knowledge test measures definite knowledge increase. Finally, a Patient Satisfaction Questionnaire reveals overall satisfaction with the intervention. CONCLUSION: This project, which proved beneficial to our patient population, could be duplicated in other clinics. The algorithm "How do I choose a dialysis bag" and the slides of the educational sessions can be shared with PD nurses across the country for the benefit of PD patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".