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Record W4406120359 · doi:10.1007/s13187-024-02564-0

Effect of Chemotherapy Patient Education Using the Teach-Back Method on Symptom Management and Quality of Life: A Randomized Controlled Trial

2025· article· en· W4406120359 on OpenAlexaboutno aff
Belkis Gullu Gucuyener, Bilgi Gülseven Karabacak

Bibliographic record

VenueJournal of Cancer Education · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersMarmara Üniversitesi
KeywordsMedicineRandomized controlled trialQuality of life (healthcare)Intervention (counseling)ChemotherapyPhysical therapyPatient educationStatistical significanceFamily medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.429
Teacher spread0.415 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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