The effect of teach‐back versus pictorial image educational methods on knowledge of renal dietary restrictions in elderly hemodialysis patients with low baseline health literacy
Bibliographic record
Abstract
INTRODUCTION: Adherence to renal dietary restrictions is an important method for minimizing complications in dialysis patients. This study aimed to investigate the effect of teach-back versus pictorial image educational methods on knowledge of renal dietary restrictions among elderly hemodialysis patients in Iran. Selected markers of diet and kidney function were also measured. METHODS: Sixty-nine elderly hemodialysis patients with a low level of health literacy were randomly divided into three groups: pictorial image education, teach-back education, and usual care (controls). The intervention groups received diet education comprising four 20-30 min sessions. Subsequently, nutrition knowledge was assessed in each of the three groups by questionnaire before and 2 months after the intervention. Blood laboratory indices were obtained from the patients' medical records and compared before and 2 months after the educational intervention. FINDINGS: There were significant differences in the mean nutritional knowledge scores between the two intervention groups and the controls (p < 0.001). Nutrition knowledge scores were higher after educational sessions incorporating images compared to those using a teach-back strategy. DISCUSSION: Nutrition educational strategies utilizing either pictorial images or teach-back techniques increased knowledge relating to renal nutrition.
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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 | 0.001 |
| 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) | 0.002 | 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".