Effectiveness of Web-Based Nutrition Education for Chronic Kidney Disease Patients
Bibliographic record
Abstract
Background: In the early stages of chronic kidney disease (CKD), encouraging health behaviors can help prevent the progression of kidney disease leading to eventual kidney failure. The studies of health education using computer technology have been greatly developed, especially web-based nutrition education.Objective: To determine the effectiveness of a nutrition education website for CKD patients.Method: The design of this quasi-experimental research was a group pre-test/post-test. The participants were pre-dialysis CKD patients who were enrolled on the developed website www.banraktai.com. The participants were required to complete an eating behavior questionnaire and knowledge test. They accessed the website for eight weeks, and at week 8, they completed the eating behavior questionnaire and knowledge test again. The main outcomes were the changes in scores of nutrition knowledge and eating behavior that were compared between the baseline and after the intervention using the paired t-test. The correlation between nutrition knowledge scores and eating behavior scores was determined using Spearman’s correlation coefficient.Results: There were 44 participants that completed the study. The results showed that the participants had significant improvement in both knowledge scores and consumption behavior scores (p < 0.001 and p = 0.041, respectively). However, there was no correlation between the nutrition knowledge scores and the eating behavior scores.Conclusions: Web-based nutrition education can improve knowledge scores but is not effective enough to encourage and motivate CKD patients to make eating behavior changes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".