Effect of Chemotherapy Patient Education Using the Teach-Back Method on Symptom Management and Quality of Life: A Randomized Controlled Trial
Bibliographic record
Abstract
This study aimed to evaluate the impact of the teach-back method in managing chemotherapy symptoms and improving quality of life. A secondary aim was to develop more effective care and education frameworks for cancer treatment. A single-center, randomized controlled trial was conducted with 80 patients who received chemotherapy between June 2022 and May 2023. Patients in the intervention group were educated about the chemotherapy process using the teach-back method, while those in the control group received standard education. Data were collected using a participant information form, the Edmonton Symptom Assessment Scale (ESAS), and the EQ-5D Quality of Life Scale. Statistical significance was accepted as p < 0.05 for all tests. In both groups, EQ-5D scores increased with the number of chemotherapy cycles, indicating a negative impact on quality of life. However, this increase was smaller in the intervention group. As the number of cycles increased, the intervention group scored lower on the Edmonton Symptom Assessment Scale compared to the control group. The results of the study show that using the teach-back method in patient education is effective in the management of chemotherapy-related symptoms and improving overall quality of life.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".