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Research article
Assessing genetic and genomic literacy concepts among Albanian nursing and midwifery students: a cross-sectional study
Elona Gaxhja, Mitilda Gugu, Angelo Dante, Armelda Teta, Armela Kapaj, Liljana Ramasaco
J Educ Eval Health Prof. 2025;22:13.   Published online April 21, 2025
DOI: https://doi.org/10.3352/jeehp.2025.22.13
  • 3,962 View
  • 291 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
This study aimed to adapt and validate the Albanian version of the Genomic Nursing Concept Inventory (GNCI) and to assess the level of genomic literacy among nursing and midwifery students.
Methods
Data were collected via a monocentric online cross-sectional study using the Albanian version of the GNCI. Participants included first-, second-, and third-year nursing and midwifery students. Demographic data such as age, sex, year level, and prior exposure to genetics were collected. The Kruskal-Wallis, Mann-Whitney U, and chi-square tests were used to compare demographic characteristics and GNCI scores between groups.
Results
Among the 715 participants, most were female (88.5%) with a median age of 19 years. Most respondents (65%) had not taken a genetics course, and 83.5% had not attended any related training. The mean score was 7.49, corresponding to a scale difficulty of 24.38% correct responses.
Conclusion
The findings reveal a low foundational knowledge of genetics/genomics among future nurses and midwives. It is essential to enhance learning strategies and update curricula to prepare a competent healthcare workforce in precision health.

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Citations to this article as recorded by  
  • Genomic literacy in nursing: a systematic scoping review of the literature
    Angelo Dante, Vittorio Masotta, Ilaria Paoli, Valeria Caponnetto, Maria Caples, Mari Laaksonen, Tina Kamenšek, Cristina Petrucci, Loreto Lancia
    BMJ Open.2025; 15(7): e100054.     CrossRef
Review
Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review
Tae Won Kim
J Educ Eval Health Prof. 2023;20:38.   Published online December 27, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.38
  • 29,219 View
  • 1,518 Download
  • 59 Web of Science
  • 68 Crossref
AbstractAbstract PDFSupplementary Material
This study aims to explore ChatGPT’s (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI’s Generative Pre-trained Transformer (GPT) model. The chatbot’s applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines’ keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.

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Research article
Enhanced numeracy skills following team-based learning in United States pharmacy students: a longitudinal cohort study  
Rob Edwin Carpenter, Leanne Coyne, Dave Silberman, Jody Kyoto Takemoto
J Educ Eval Health Prof. 2022;19:29.   Published online October 27, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.29
  • 4,951 View
  • 205 Download
  • 2 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
The literature suggests that the ability to numerate cannot be fully understood without accounting for the social context in which mathematical activity is represented. Team-based learning (TBL) is an andragogical approach with theoretical links to sociocultural and community-of-practice learning. This study aimed to quantitatively explore the impact of TBL instruction on numeracy development in 2 cohorts of pharmacy students and identify the impact of TBL instruction on numeracy development from a social perspective for healthcare education.
Methods
Two cohorts of students were administered the Health Science Reasoning Test-Numeracy (HSRT-N) before beginning pharmacy school. Two years after using TBL as the primary method of instruction, both comprehensive and domain data from the HSRT-N were analyzed.
Results
In total, 163 pharmacy student scores met the inclusion criteria. The students’ numeracy skills measured by HSRT-N improved after 2 years of TBL instruction.
Conclusion
Numeracy was the most significantly improved HSRT-N domain in pharmacy students following two years of TBL instruction. Although a closer examination of numeracy development in TBL is warranted, initial data suggest that TBL instruction may be an adequate proxy for advancing numeracy in a cohort of pharmacy students. TBL may encourage a social practice of mathematics to improve pharmacy students’ ability to numerate critically.

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