Enhancing Knowledge in Adolescents Undergoing Hemodialysis in Palestine: The Impact of a Video‐Assisted Educational Program
Bibliographic record
Abstract
BACKGROUND: Hemodialysis is a common initial treatment for young individuals with end-stage renal disease. Educating these patients is crucial for improving their knowledge and well-being. Lifestyle modifications, promoted through health education, are essential for reducing hemodialysis-related complications. Although traditional face-to-face education is prevalent, video-based education offers a more convenient and cost-effective alternative with numerous benefits. OBJECTIVE: This study aimed to evaluate the impact of a video-assisted educational program on the knowledge of adolescents undergoing hemodialysis in Palestine. METHODS: We conducted a quasi-experimental, pretest-posttest intervention study with 68 adolescent patients (Aged 13-18) diagnosed with end-stage kidney disease (ESKD). Patients were divided into two groups based on their treating hospital: an experimental group (n = 34) received video-based education, whereas a control group (n = 34) received traditional face-to-face education. Knowledge in both groups was assessed using the validated Kidney Knowledge Questionnaire. RESULTS: A Generalized Estimating Equation analysis revealed a statistically significant difference in knowledge scores between the experimental and control groups across pretest, posttest, and follow-up assessments (p = 0.024). Furthermore, within both the experimental and control groups, there were statistically significant improvements in total knowledge scores from pretest to posttest and follow-up assessments (p < 0.001). CONCLUSION: Implementing effective educational interventions can enhance the knowledge of individuals undergoing hemodialysis. Therefore, we recommend using video-based instruction as a practical, easy, and engaging approach for educating hemodialysis 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".