A randomized control trial: Effects of teach back method on self-efficacy among mothers of children with congenital heart defects
Bibliographic record
Abstract
Background/Objective: Congenital Heart Defects (CHD) remain a major health concern all over the world particularly Egypt where the prevalence of CHD is 1.0 per 1,000. Nurses are instrumental in supplying information. The teach-back method is a technique used for improving patient understanding and outcomes. This study aimed to evaluate the effect of teach back method on self-efficacy and satisfaction among mothers of children with congenital heart defects.Methods: The design of this study was randomized control trail. A sample of 60 children with congenital heart defects and their mothers participated in this study. It conducted at Menofia University hospital. Tools of this study included Self Efficacy Scale; Teach back Discharge Education Audit and Satisfaction Assessment.Results: The current study revealed that the majority of nurses were unfamiliar with teach-back method and there was significant difference between mothers in the experimental and control groups regarding their self-efficacy.Conclusions: This study concluded that mothers who received discharge instructions through teach back method had increased self-efficacy and high level of satisfaction. Therefore, pediatric nurses should integrate teach back method as a routine nursing intervention in the discharge plan for children with congenital heart defects.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".