IMPACT OF AN AI-SUPPORTED FLIPPED LEARNING MODEL ON NURSING STUDENTS' BREAST SELF-EXAMINATION KNOWLEDGE AND PERFORMANCE: A RANDOMIZED CONTROLLED TRIAL
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 80
- 试验地点
- 1
- 主要终点
- Self-Breast Examination Information Form
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- Single (Outcomes Assessor)
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Being a second-year student in a nursing undergraduate program
- •Not having previously received breast examination training
- •Having signed the voluntary consent form
排除标准
- •Having any health problem that would prevent continuing to work
- •Requesting to withdraw from work voluntarily
研究组 & 干预措施
Traditional-based education
Traditional-based education-no intervention
AI-supported flipped learning model
Artificial Intelligence-Supported Flipped Learning Model Intervention
干预措施: Artificial Intelligence-Supported Flipped Learning Model-Based Breast Self-Examination Training (Behavioral)
结局指标
主要结局
Self-Breast Examination Information Form
时间窗: 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.
This form, prepared by researchers based on the literature to determine students' knowledge level regarding self-breast examination, consists of 20 items. The form includes 17 theoretical questions and 3 case questions. Students will be asked to mark either "true" or "false" for each item. Items will be evaluated by giving "1 point" for a correct answer and "0 points" for an incorrect answer. The lowest possible score on the form is 0, and the highest possible score is 20. The opinions of 10 independent experts were consulted to evaluate the form in terms of its scope, language, comprehensibility, expression, and scientific adequacy. This form will be administered to all students as a pre-test and post-test, both before and after the application.
Breast Self-Examination Skill Checklist
时间窗: This form will be administered to all students as a post-test 2 weeks after the application.
To assess students' self-breast examination skills, a structured skill checklist based on the literature will be used. The form consists of a total of 20 questions. While students perform the self-breast examination, an independent evaluator will ask them to indicate whether the student performed the skills or not for each item by marking either "completed" or "did not complete". Items will be evaluated by giving "1 point" for "completed" and "0 points" for "did not complete". The lowest possible score on the form is 0, and the highest possible score is 20. The opinions of 10 independent experts were consulted to evaluate the form in terms of its scope, language, comprehensibility, expression, and scientific adequacy. This form will be administered to all students as a post-test after the application.
次要结局
未报告次要终点
研究者
BETUL SAHIN KILINC
Lecturer
Baskent University