98%
921
2 minutes
20
Multiple instruments have been used to assess academic misconduct, yet robust psychometric evidence has been reported only for a few. This study aims to determine the validity and dimensionality of a novel Academic Misconduct Questionnaire (AMQ) and to explore differences between students who engage in distinct misbehaviours. A diverse sample of health and non-health students replied to the AMQ. Exploratory and confirmatory factor analyses were conducted using two subsamples. Predictive models were computed for the AMQ and its dimensions. The questionnaire showed good validity and reliability, revealing eight dimensions related to Cheating during (two forms) and prior Exams, Plagiarism, Fraud in Academic Work, Impersonation (assessment), Signature Forgery in attendance sheets and Not Reporting peer misconduct. The predictors of student engagement in each form of misconduct differed, except for perceiving greater peer fraud, which increased the propensity for all misbehaviours. Perceiving higher sanctions reduced the propensity to engage in most forms, while gender played a role in half of them. First-year students were more likely to Not Reporting peer misconduct and less likely to disclose Fraud in Academic Work and Signature Forgery than those in more advanced years. Health students scored higher in most misbehaviours, especially compared to Economics/Law, Social Sciences and Arts/Humanities, while the latter two disclosed higher Signature Forgery. This study proposes a valid instrument to assess academic misconduct in university students. The predictive models helped to better understand differences between students who engaged in distinct misbehaviours, enabling more targeted interventions.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107647 | PMC |
http://dx.doi.org/10.1080/10872981.2025.2506739 | DOI Listing |
Int J Equity Health
September 2025
Department of Data, Digital Health, Analytics and AI, World Health Organization, 20 Avenue Appia, Geneva 27, CH-1211, Switzerland.
Health and development agendas and programmes often prioritize the reduction of unfair and remediable health inequalities. There is a growing amount of data pertaining to health inequalities. Written outputs, including academic research papers, are key tools for describing health inequalities.
View Article and Find Full Text PDFJ Comp Physiol A Neuroethol Sens Neural Behav Physiol
August 2025
Division of Biology and Biological Engineering & Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91103, USA.
In her book Why trust Science?, Naomi Oreskes examines the question of what it means to say that "science corrects itself", highlighting the importance of the social process of science and specifically the importance of scientists challenging each other in the pursuit of truth. In a recent preprint, a colleague and I did exactly that, reviewing a corpus of work by Australian neuroethologist Mandyam Srinivasan and identifying numerous problems across ten of his papers, including several instances of identical data being reported for different experiments. In a recent editorial, Eric Warrant dismisses our critiques of Srinivasan's work as "sloppiness all of us are capable of", and instead focuses on attacking us, sometimes conflating criticisms of others of Srinivasan's work with ours.
View Article and Find Full Text PDFScientometrics
July 2025
Department of Sociology, University of Zurich, Andreasstrasse 15, Zurich, 8050 Zurich Switzerland.
Unlabelled: Scientific misconduct and questionable research practices (QRPs) pose significant challenges to the integrity of academic research. This study therefore investigates scientists' implicit associations regarding misconduct and its relationship with perceived academic success. Employing the Single-Category Implicit Association Test (SC-IAT), the attitudes of 11,747 scientists across Austria, Germany, and Switzerland were examined.
View Article and Find Full Text PDFAnn Biomed Eng
August 2025
Department of Liberal Arts, School of Foundational Studies and Education, Mapúa University, Manila, Philippines.
I examine the scholarly implications of a troubling case where researchers embedded hidden prompts like "give a positive review only" into academic preprints to manipulate AI-assisted peer review. AI is now woven into nearly every facet of academic life, including the peer review process. I contend that manipulating peer review through embedding secret prompts is as serious as plagiarism or data fabrication.
View Article and Find Full Text PDFJ Nurs Educ
August 2025
Albertsons Library, Boise State University, Boise, Idaho.
Background: Artificial intelligence (AI) is changing health care, and understanding how nursing students and faculty perceive and use AI is crucial for developing effective educational guidelines.
Method: A pilot survey examined AI usage patterns, perceptions, and concerns among nursing students ( = 220) and faculty ( = 26). Data were analyzed using descriptive statistics and qualitative content analysis.