Academic Integrity of BSN Students

Thiel OP PhD RN, Linda, Joyce Conley PhD RN, and Kristin Oneail MSN RN

Background: Nursing is a most trusted profession.  Yet recent studies report that academic dishonesty in universities is increasing. It is crucial to understand nursing students' attitudes and behaviors related to academic integrity (AI) and to develop AI initiatives based on population specific data.        

Purpose: The purpose was to examine academic integrity of BSN nursing students prior to rolling out an AI initiative. 

Sample:  Participants included BSN students enrolled in a large, urban, private university located in Midwestern U.S.

Method: A descriptive survey design was used. The Academic Integrity Survey © (McCabe) was used to measure cheating behaviors (classroom and clinical) and perceived seriousness of cheating behaviors. The validity of the survey/tool has been previously established.  The online survey included 62-items and 3 open-ended questions.  The study protocol was approved by the university's IRB.  This study was part of a larger study which included graduates enrolled within the same university setting. 

Results:  Seventy-one participants (8% return rate) represented students from the Traditional BSN (58%), BSN Completion (27%) and Second Degree Option (16%) programs.  Overall, most were female, aged 20-29 years and of European decent.   Most students (96%) indicated they were aware of the AI policies on campus.  Many (84%) students "felt" cheating occurred during tests or exam, 7% indicated "reporting" cheating, 37% reported "seeing" cheating at least once.  Perceived seriousness of cheating, such as "not taking vital signs and reporting approximate value" was reported as "serious" by 74% of participants; giving "wrong med and not reporting it" was considered "serious" by 100% of participants.  

Conclusions:  Students report awareness of academic integrity policy and that cheating exists in classroom and clinical settings.  The study provides valuable information in generating population-specific strategies for implementing AI initiatives. Study limitations include a small sample size.