AI-SUPPORTED FLIPPED LEARNING IN BREAST SELF-EXAMINATION TRAINING
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Artificial Intelligence-Supported Flipped Learning Model-Based Breast Self-Examination Training.
- Who it may be relevant to
- Registry conditions: Artifical Intelligence, Nursing Education, Breast Self-Examination. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Turkey (Türkiye)
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
IMPACT OF AN AI-SUPPORTED FLIPPED LEARNING MODEL ON NURSING STUDENTS' BREAST SELF-EXAMINATION KNOWLEDGE AND PERFORMANCE: A RANDOMIZED CONTROLLED TRIAL
Overview
The global increase in cancer cases has made breast cancer the second most common cancer after lung cancer and a primary health problem among women. Early diagnosis is the most critical factor in improving survival rates and quality of life in breast cancer. Breast self-examination (BSE), which enables individuals to notice changes in their own breast tissue during the early diagnosis process, is a low-cost and effective awareness method. It is essential that nurses, who play a key role in raising public awareness on this issue, and nursing students, who are the future healthcare professionals, have sufficient knowledge and practical skills in BSE. However, the literature shows that even if students have theoretical knowledge, their application rates are low. In this context, the "AI-Supported Flipped Learning" model, which goes beyond traditional methods and supports active learning, personalized feedback, and digital literacy, has the potential to be an innovative solution in nursing education. Objective: This study aims to evaluate the effect of AI-supported flipped learning model and traditional education on the knowledge levels and performance skills of nursing students regarding BSE knowledge and skills.
Interventions
- Behavioral Artificial Intelligence-Supported Flipped Learning Model-Based Breast Self-Examination Training
An AI-supported reverse learning model-based intervention for breast self-examination training. This intervention has not been seen in any previous studies.
Primary outcome measures
- Self-Breast Examination Information Form [Time frame: This form will be administered to all students as a pre-test and post-test, both before and after the procedure. Before the procedure and 2 weeks after the procedure.]
- Breast Self-Examination Skill Checklist [Time frame: This form will be administered to all students as a post-test 2 weeks after the application.]
Eligibility criteria
Inclusion criteria
- Being a second-year student in a nursing undergraduate program
- Not having previously received breast examination training
- Having signed the voluntary consent form
Exclusion criteria
- Having any health problem that would prevent continuing to work
- Requesting to withdraw from work voluntarily
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Other
Study locations
Turkey (Türkiye) · 1 center
- Baskent University — Ankara
Identifiers
NCT: NCT07562321 · 17162298.600-89