Catheter Care in a Hemodialysis Unit: “Do It Daily,” a Multimodal Patient Education Approach
Bibliographic record
Abstract
Background: Central venous catheters or CVLs are the leading cause of mortality and morbidity in the dialysis population. The HDU personnel wanted to empower the patients to manage and care for their catheters. We developed a standardized educational framework in collaboration with healthcare professionals and the Patient Experience Panel. Objectives: Our ultimate goal was to reduce the rates of catheter infections in our hemodialysis unit by improving patient’s knowledge, confidence, and skills related to catheter self-care. Our immediate goal was to improve patients’ catheter care knowledge and skills and to standardize and optimize nursing skills and knowledge Methods: The patients were given a pre-education survey to establish baseline knowledge, attitudes and skill levels. Educational materials were developed based on the patients’ feedback, knowledge and needs, and also on practice guidelines and best practice recommendations from CDC, KDIGO, and ORN. Nursing education involved updating nursing policies, and a nursing catheter care certification program. Educational materials included a video, posters, pamphlets and fridge magnets using the catchphrase “Do it Daily”. The acronym “DAILY” represents the following: D for “dressing, soiled wet or damaged”, A for “any rash, itching or broken skin, I for “increased pain at catheter site, L for “length of catheter changed” and Y for “you have redness, pus, fever”. Post education surveys were conducted to assess the patients’ knowledge and skill levels. Results: Thirty-three patients completed baseline surveys, education programs and post education surveys. No significant difference in proportion of patients answering yes to Knowledge or Skill assessment pre- and post-education survey. Although, there was a trend of patients stating they had received enough teaching about catheter care, knew how to keep catheter clean and dry, recognize complications and not adjust catheter by themselves. Eighty-nine percent of patients found the education/training easy to understand. Conclusions: Use of Multi-Modal patient education material is an easy to understand and feasible tool to help patients understand proper care of their dialysis catheters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".