Journal of Educational Evaluation for Health Professions

Reviews

Levels, antecedents, and consequences of critical thinking among clinical nurses: a quantitative literature review: Yongmi Lee, Younjae Oh; J Educ Eval Health Prof. 2020;17:26. Published online September 7, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.26

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The purpose of this study was to obtain a more comprehensive understanding of critical thinking within the clinical nursing context. In this review, we addressed the following specific research questions: what are the levels of critical thinking among clinical nurses?; what are the antecedents of critical thinking?; and what are the consequences of critical thinking? A narrative literature review was applied in this study. Thirteen articles published from July 2013 to December 2019 were appraised since the most recent scoping review on critical thinking among nurses was conducted from January 1999 to June 2013. The levels of critical thinking among clinical nurses were moderate or high. Regarding the antecedents of critical thinking, the influence of sociodemographic variables on critical thinking was inconsistent, with the exception that levels of critical thinking differed according to years of work experience. Finally, little research has been conducted on the consequences of critical thinking and related factors. The above findings highlight the levels, antecedents, and consequences of critical thinking among clinical nurses in various settings. Considering the significant association between years of work experience and critical thinking capability, it may be effective for organizations to deliver tailored education programs on critical thinking for nurses according to their years of work experience.

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Multilevel Modeling of Individual and Group Level Influences on Critical Thinking and Clinical Decision-Making Skills among Registered Nurses: A Study Protocol
Nur Hidayah Zainal, Kamarul Imran Musa, Nur Syahmina Rasudin, Zakira Mamat
Healthcare.2023; 11(8): 1169. CrossRef
Critical thinking among clinical nurses and related factors: A survey study in public hospitals
Eylül Urhan, Esperanza Zuriguel‐Perez, Arzu Kader Harmancı Seren
Journal of Clinical Nursing.2022; 31(21-22): 3155. CrossRef
Impact of Nurse–Physician Collaboration, Moral Distress, and Professional Autonomy on Job Satisfaction among Nurses Acting as Physician Assistants
Yunmi Kim, Younjae Oh, Eunhee Lee, Shin-Jeong Kim
International Journal of Environmental Research and Public Health.2022; 19(2): 661. CrossRef
Development and validation of a script concordance test to assess biosciences clinical reasoning skills: A cross-sectional study of 1st year undergraduate nursing students
Catherine Redmond, Aiden Jayanth, Sarah Beresford, Lorraine Carroll, Amy N.B. Johnston
Nurse Education Today.2022; 119: 105615. CrossRef
The nursing critical thinking in clinical practice questionnaire for nursing students: A psychometric evaluation study
Esperanza Zuriguel-Pérez, María-Teresa Lluch-Canut, Montserrat Puig-Llobet, Luis Basco-Prado, Adrià Almazor-Sirvent, Ainoa Biurrun-Garrido, Mariela Patricia Aguayo-González, Olga Mestres-Soler, Juan Roldán-Merino
Nurse Education in Practice.2022; 65: 103498. CrossRef
Transition shock, preceptor support and nursing competency among newly graduated registered nurses: A cross-sectional study
Feifei Chen, Yuan Liu, Xiaomin Wang, Hong Dong
Nurse Education Today.2021; 102: 104891. CrossRef

Nurse educators’ experiences with student incivility: a meta-synthesis of qualitative studies: Eun-Jun Park, Hyunwook Kang; J Educ Eval Health Prof. 2020;17:23. Published online August 11, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.23

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This study aimed to synthesize the best available qualitative research evidence on nurse educators’ experiences with student incivility in undergraduate nursing classrooms. A meta-synthesis of qualitative evidence using thematic synthesis was conducted. A systematic search was performed of 12 databases for relevant literature published by March 31, 2019. Two reviewers independently conducted critical quality appraisals using the checklist for qualitative research developed by the Joanna Briggs Institute. Eleven studies that met the inclusion criteria were selected for review. From the pooled study findings, 26 descriptive themes were generated and categorized into the following 5 analytical themes: (1) factors contributing to student incivility, (2) management of student incivility, (3) impact: professional and personal damage, (4) impact: professional growth, and (5) initiatives for the future. Many nurse educators became confident in their role of providing accountability as both educators and gatekeepers and experienced professional growth. However, others experienced damage to their personal and professional life and lost their motivation to teach. Nurse educators recommended the following strategies for preventing or better managing student incivility: institutional efforts by the university, unified approaches for student incivility within a nursing program, a faculty-to-faculty network for mentoring, and better teaching and learning strategies for individual educators. These strategies would help all nurse educators experience professional growth by successfully preventing and managing student incivility.

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American Academy of Nursing Expert Panel Consensus Statement on leveraging equity in policy to improve recognition and treatment of mental health, substance use disorders, and nurse suicide
JoEllen Schimmels, Carla Groh, Michael Neft, Lucia Wocial, Cara Young, Judy E. Davidson
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Experiences of undergraduate nursing students with faculty incivility in nursing classrooms: A meta-aggregation of qualitative studies
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Nurse Education in Practice.2021; 52: 103002. CrossRef
Can nursing educators learn to trust the world’s most trusted profession?
Philip Darbyshire, David R. Thompson
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Changes in the accreditation standards of medical schools by the Korean Institute of Medical Education and Evaluation from 2000 to 2019: Hyo Hyun Yoo, Mi Kyung Kim, Yoo Sang Yoon, Keun Mi Lee, Jong Hun Lee, Seung-Jae Hong, Jung –Sik Huh, Won Kyun Park; J Educ Eval Health Prof. 2020;17:2. Published online April 7, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.2

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This review presents information on changes in the accreditation standards of medical schools in Korea by the Korean Institute of Medical Education and Evaluation (KIMEE) from 2000 to 2019. Specifically, the following aspects are explained: the development process, setting principles and directions, evaluation items, characteristics of the standards, and validity testing over the course of 4 cycles. The first cycle of accreditation (2000–2005) focused on ensuring the minimum requirements for the educational environment. The evaluation criteria emphasized the core elements of medical education, including facilities and human resources. The second cycle of accreditation (2007–2010) emphasized universities’ commitment to social accountability and the pursuit of excellence in medical education. It raised the importance of qualitative standards for judging the content and quality of education. In the post-second accreditation cycle (2012–2018) which means third accreditation cycle, accreditation criteria were developed to standardize the educational environment and programs and to be used for curriculum development in order to continually improve the quality of basic medical education. Most recently, the ASK 2019 (Accreditation Standards of KIMEE 2019) accreditation cycle focused on qualitative evaluations in accordance with the World Federation of Medical Education’s accreditation criteria to reach the international level of basic medical education, which emphasizes the need for a student-centered curriculum, communication with society, and evaluation through a comprehensive basic medical education course. The KIMEE has developed a basic medical education evaluation and accreditation system in a step-by-step manner, as outlined above. Understanding previous processes will be helpful for the future development of accreditation criteria for medical schools in Korea.

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Quality and constructed knowledge: Truth, paradigms, and the state of the science
Janet Grant, Leonard Grant
Medical Education.2023; 57(1): 23. CrossRef
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Hiroko Mori, Masashi Izumiya, Mikio Hayashi, Masato Eto
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Accreditation standards items of post-2nd cycle related to the decision of accreditation of medical schools by the Korean Institute of Medical Education and Evaluation
Kwi Hwa Park, Geon Ho Lee, Su Jin Chae, Seong Yong Kim
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Shuliweeh Alenezi, Ayman Al-Eadhy, Rana Barasain, Trad S. AlWakeel, Abdullah AlEidan, Hadeel N. Abohumid
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MohamedH Ahmed, MohamedElhassan Abdalla, MohamedH Taha
Journal of Family Medicine and Primary Care.2020; 9(8): 3820. CrossRef

How to execute Context, Input, Process, and Product evaluation model in medical health education: So young Lee, Jwa-Seop Shin, Seung-Hee Lee; J Educ Eval Health Prof. 2019;16:40. Published online December 28, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.40

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Improvements to education are necessary in order to keep up with the education requirements of today. The Context, Input, Process, and Product (CIPP) evaluation model was created for the decision-making towards education improvement, so this model is appropriate in this regard. However, application of this model in the actual context of medical health education is considered difficult in the education environment. Thus, in this study, literature survey of previous studies was investigated to examine the execution procedure of how the CIPP model can be actually applied. For the execution procedure utilizing the CIPP model, the criteria and indicators were determined from analysis results and material was collected after setting the material collection method. Afterwards, the collected material was analyzed for each CIPP element, and finally, the relationship of each CIPP element was analyzed for the final improvement decision-making. In this study, these steps were followed and the methods employed in previous studies were organized. Particularly, the process of determining the criteria and indicators was important and required a significant effort. Literature survey was carried out to analyze the most widely used criteria through content analysis and obtained a total of 12 criteria. Additional emphasis is necessary in the importance of the criteria selection for the actual application of the CIPP model. Also, a diverse range of information can be obtained through qualitative as well as quantitative methods. Above all, since the CIPP evaluation model execution result becomes the basis for the execution of further improved evaluations, the first attempt of performing without hesitation is essential.

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Changes in the accreditation standards of medical schools by the Korean Institute of Medical Education and Evaluation from 2000 to 2019
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What should medical students know about artificial intelligence in medicine?: Seong Ho Park, Kyung-Hyun Do, Sungwon Kim, Joo Hyun Park, Young-Suk Lim; J Educ Eval Health Prof. 2019;16:18. Published online July 3, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.18

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48 Citations

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Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.

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Yan Liu, Maojun Zhang, Zhiwei Zhong, Xiangrong Zeng
Medical Physics.2023; 50(3): 1528. CrossRef
Clinical informatics training in medical school education curricula: a scoping review
Humairah Zainal, Joshua Kuan Tan, Xin Xiaohui, Julian Thumboo, Fong Kok Yong
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Humairah Zainal, Xin Xiaohui, Julian Thumboo, Fong Kok Yong, Conor Gilligan
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Lukas Weidener, Michael Fischer
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Ali Jasem Buabbas, Brouj Miskin, Amar Ali Alnaqi, Adel K. Ayed, Abrar Abdulmohsen Shehab, Shabbir Syed-Abdul, Mohy Uddin
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G. Caparrós Galán, F. Sendra Portero
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Davide Luciani, Alessandro Magrini, Carlo Berzuini, Antonello Gavazzi, Paolo Canova, Tiziano Barbui, Guido Bertolini
Expert Systems with Applications.2022; 189: 116066. CrossRef
SHIFTing artificial intelligence to be responsible in healthcare: A systematic review
Haytham Siala, Yichuan Wang
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AUGMENTING CBME CURRICULUM WITH ARTIFICIAL INTELLIGENCE COURSES – A FUTURISTIC APPROACH.
Yogesh Bahurupi, Ashwini A Mahadule, Prashant M Patil, Vartika Saxena
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Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future?
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Kathleen Gray, John Slavotinek, Gerardo Luis Dimaguila, Dawn Choo
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Nikitha Vobugari, Vikranth Raja, Udhav Sethi, Kejal Gandhi, Kishore Raja, Salim R. Surani
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Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum
Joel Grunhut, Oge Marques, Adam T M Wyatt
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Faisal A. Nawaz, Austin A. Barr, Monali Y. Desai, Christos Tsagkaris, Romil Singh, Elisabeth Klager, Fabian Eibensteiner, Emil D. Parvanov, Mojca Hribersek, Maria Kletecka-Pulker, Harald Willschke, Atanas G. Atanasov
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Naoto Nakagawa, Keita Odanaka, Hiroshi Ohara, Shigeki Kisara
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Advances in Medical Education and Practice.2022; Volume 13: 927. CrossRef
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David Shalom Liu, Jake Sawyer, Alexander Luna, Jihad Aoun, Janet Wang, Lord Boachie, Safwan Halabi, Bina Joe
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Components of the item selection algorithm in computerized adaptive testing: Kyung (Chris) Tyek Han; J Educ Eval Health Prof. 2018;15:7. Published online March 24, 2018; DOI: https://doi.org/10.3352/jeehp.2018.15.7

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Abstract PDF Supplementary Material

Computerized adaptive testing (CAT) greatly improves measurement efficiency in high-stakes testing operations through the selection and administration of test items with the difficulty level that is most relevant to each individual test taker. This paper explains the 3 components of a conventional CAT item selection algorithm: test content balancing, the item selection criterion, and item exposure control. Several noteworthy methodologies underlie each component. The test script method and constrained CAT method are used for test content balancing. Item selection criteria include the maximized Fisher information criterion, the b-matching method, the astratification method, the weighted likelihood information criterion, the efficiency balanced information criterion, and the KullbackLeibler information criterion. The randomesque method, the Sympson-Hetter method, the unconditional and conditional multinomial methods, and the fade-away method are used for item exposure control. Several holistic approaches to CAT use automated test assembly methods, such as the shadow test approach and the weighted deviation model. Item usage and exposure count vary depending on the item selection criterion and exposure control method. Finally, other important factors to consider when determining an appropriate CAT design are the computer resources requirement, the size of item pools, and the test length. The logic of CAT is now being adopted in the field of adaptive learning, which integrates the learning aspect and the (formative) assessment aspect of education into a continuous, individualized learning experience. Therefore, the algorithms and technologies described in this review may be able to help medical health educators and high-stakes test developers to adopt CAT more actively and efficiently.

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Merve ŞAHİN KÜRŞAD
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Ioannis Tsaousis, Georgios D. Sideridis, Hannan M. AlGhamdi
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Kyung (Chris) T. Han, Dimiter M. Dimitrov, Faisal Al-Mashary
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