Baskent University
Ankara, 06790, Turkey (Türkiye)
Location status: Recruiting
Location contact
Betul Sahin-Kilinc
CONTACT
Betül Şahin Kılınç
PRINCIPAL_INVESTIGATOR
NCT Number: NCT07562321
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.
Interested in participating?
Request InfoAll sexes
Interventional
Not applicable
Ankara, 06790, Turkey (Türkiye)
Location status: Recruiting
Betul Sahin-Kilinc
CONTACT
Betül Şahin Kılınç
PRINCIPAL_INVESTIGATOR
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
An AI-supported reverse learning model-based intervention for breast self-examination training. This intervention has not been seen in any previous studies.
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.
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.
Time frame: 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.
Contact information is provided by the study sponsor or research team.
Baskent University
Other
IMPACT OF AN AI-SUPPORTED FLIPPED LEARNING MODEL ON NURSING STUDENTS' BREAST SELF-EXAMINATION KNOWLEDGE AND PERFORMANCE: A RANDOMIZED CONTROLLED TRIAL
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Published trials that share one or more normalized conditions with this study.
NCT07726290
Artifical Intelligence, Behavior
Cairo, Egypt
View Trial DetailsNCT07634913
Artifical Intelligence, Bacterial Infections
Guangzhou, Guangdong, China
View Trial DetailsNCT07284550
Aortic Valve Disease, Aortic Valve Stenosis
Innsbruck, Austria
View Trial DetailsNCT07580612
Artifical Intelligence, Basal Ganglia Diseases
Leuven, Belgium
View Trial Details