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Showing 5 results for Academic

Hakimeh Eskandari Sabzi, Maryam Heidari, Shahrzad Nezarat, Mahsa Mousavi, Mohammad Amin Harizavi, Atefeh Zahedi,
Volume 9, Issue 3 (10-2016)
Abstract

Ethics of care are the central core of nursing values and a fundamental concept in the nursing profession. Since the outlook of nurses on ethics can affect the quality of care, the present study was conducted to investigate the attitude of nursing students to codes of ethics for nurses, their commitment to ethics of care, and academic dishonesty in Abadan School of Medical Sciences. 

This descriptive analytical study was performed among 150 nursing students of Abadan School of Medical Sciences by census sampling in 2016. After giving their informed consent, the students completed the questionnaire designed by McCrink in 3 sections: 1) demographic information, 2) attitude to ethics codes, commitment to ethics of care and neutralization behaviors, and 3) outlook on academic dishonesty. Data were analyzed using descriptive statics, chi-square test and Pearson’s correlation coefficient in SPSS version 21.

The results showed that the students had the most positive attitude to ethics codes and commitment to ethics of care, and the most negative attitude to neutralization behaviors. Less than half of the participants had a negative outlook on academic dishonesty. Moreover, about 40 percent of the students reported academic dishonesty among their classmates. Ethics codes are moral values in academic and clinical settings and should therefore be considered as major components of initial nursing education programs. Additionally, it seems necessary to make efforts in order to change nursing students’ attitude toward academic dishonesty.


Mojtaba Fazel, Elham Afshari,
Volume 13, Issue 0 (3-2020)
Abstract

The phrase "Academic mobbing" can be described as character assassination or psychological harassment against a colleague. According to many published reports, individuals in any workplace, including the academic environment, may be targets of coworkers' antisocial behaviors such as accusation, humiliation, emotional abuse, and general offences. In addition to personal negative outcomes including decreased job satisfaction, increased occupational stress, and higher risk for anxiety and depression; academic mobbing can lead to decreased efficacy of the organization to reach its targets. Job dissatisfaction leads to decrease effort of faculties in performing educational and scientific activities that has indirect consequences on community. The direct effect of dissatisfaction of faculties would be decreased quality and quantity of educational services to students. Since the first steps to systematically deal with any social issue, including academic mobbing is understanding the nature and characteristics of the situation, the current review tends to introduce and establish the characteristics of academic mobbing as well as the role of the authorities in preventing or resolving the problem.

Mr Farhad Khormaee, Khatoun Mahmoudnezhad,
Volume 14, Issue 0 (3-2021)
Abstract

Academic dishonesty is one of the important challenges of educational centers. In the present study, the role of moral disengagement mediators’ in the relationship between moral characters and academic dishonesty was investigated. The present study is a correlation study. The statistical population included all students of Shiraz University and the participants were 246 students selected by random cluster sampling. Moral disengagement and academic dishonesty scales and moral characters questionnaire were used to measure the research variables. Structural Equation Modeling was performed using AMOS software to analyze the research data. The results of the structural equation model showed that positive moral characters are directly related to academic dishonesty, also negative moral characters has a significant relationship with academic dishonesty directly and with mediating of moral disengagement. Moral disengagement has been directly predictor of academic dishonesty in students, too. According to the findings, it can be concluded that positive moral characters directly and negative moral characters directly and with mediating of moral disengagement can predict academic dishonesty. Moral disengagement was predictor of academic dishonesty in students, too.

Mozaffar Ghaffari, Ahmad Esmali, Vahid Abdolmanafi, Mahtab Aligolipour,
Volume 16, Issue 1 (3-2023)
Abstract

The prevalence of academic cheating in educational centers and institutions leads to inefficiency and incapacity of graduates. Accordingly, the current study aimed to design a structural model for academic cheating in medical students based on moral metacognition, moral identity, and moral potency. This correlational study was done using structural equation modeling. The statistical population of the study included the students of Tabriz University of Medical Sciences in 2022, and 350 students were selected for the study using simple random sampling method. Data were collected through the Academic Cheating Scale (ACS) (Parks-Leduc, Guay and Mulligan, 2022), Moral Metacognition Scale (McMahon and Good, 2016), Moral Identity Questionnaire (MIQ) (Black & Reynolds, 2016), and Moral Potency Questionnaire (Hannah and Avolio, 2010). Data were analyzed using Bootstrap, Sobel, and Pearson’s correlation coefficient tests via SPSS and AMOS, version 24. The results indicated that the direct effect of moral potency (-0.34), moral identity (-0.25), and moral metacognition (-0.29) was significant on estimating academic cheating in students. The indirect effect of moral identity (-1.97) and moral metacognition (-2.06) with the mediating role of moral potency on students’ academic cheating was significant. Considering the mediating effect of moral potency in the academic cheating model, it seems that moral potency plays a role in increasing the effects of moral metacognition and moral identity on reducing academic cheating.

Amirmohammad Azarakhsh, Mohammadreza Dinmohammadi, Kian Nouroozi Tabrizi, Kowsar Nouri,
Volume 17, Issue 0 (12-2024)
Abstract

In recent years, artificial intelligence (AI) has significantly impacted the publication of research articles, transforming the landscape of academic writing and dissemination. However, the integration of AI in this process presents significant ethical challenges that require careful consideration. This review study utilized a comprehensive search strategy, employing keywords such as "artificial intelligence," "publication ethics," "ethical challenges," "academic integrity," and "research dissemination" to identify relevant articles in scientific databases including PubMed, Scopus, CINAHL, and Google Scholar. The search included articles published between 2010 and 2024 in both English and Persian. Research articles, systematic reviews, and case reports that included the specified keywords in their titles and abstracts were selected. A total of 150 articles were screened, and 50 relevant studies were included for detailed analysis. The analysis identified several ethical challenges associated with the use of AI in academic publishing. Concerns regarding academic integrity are paramount, as AI-generated content can blur the lines between original research and automated writing, raising concerns about authorship and plagiarism. Furthermore, the reliance on AI tools for data analysis and manuscript preparation can raise questions about the accuracy and validity of research findings. additionally, the potential for bias embedded within AI algorithms is a significant concern, as it can influence the selection of research topics, the framing of research questions, and even the peer review process. The lack of transparency in AI-driven editorial processes can further undermine trust in academic publishing. This review underscores the urgent need for robust ethical frameworks and regulations to guide the responsible use of AI in academic publishing. Increased awareness and training among researchers and editors regarding the ethical implications of AI are crucial. Interdisciplinary collaborations are essential to address these challenges effectively and ensure the integrity and trustworthiness of academic research in the AI era.
 


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