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Equal Z standard-setting method to estimate the minimum number of panelists for a medical school’s objective structured clinical examination in Taiwan: a simulation study
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Ying-Ying Yang
 , Pin-Hsiang Huang, Ling-Yu Yang, Chia-Chang Huang, Chih-Wei Liu, Shiau-Shian Huang, Chen-Huan Chen, Fa-Yauh Lee, Shou-Yen Kao, Boaz Shulruf
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J Educ Eval Health Prof. 2022;19:27. Published online October 17, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.27
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 Abstract
 PDF Supplementary Material
- Purpose
Undertaking a standard-setting exercise is a common method for setting pass/fail cut scores for high-stakes examinations. The recently introduced equal Z standard-setting method (EZ method) has been found to be a valid and effective alternative for the commonly used Angoff and Hofstee methods and their variants. The current study aims to estimate the minimum number of panelists required for obtaining acceptable and reliable cut scores using the EZ method.
Methods
The primary data were extracted from 31 panelists who used the EZ method for setting cut scores for a 12-station of medical school’s final objective structured clinical examination (OSCE) in Taiwan. For this study, a new data set composed of 1,000 random samples of different panel sizes, ranging from 5 to 25 panelists, was established and analyzed. Analysis of variance was performed to measure the differences in the cut scores set by the sampled groups, across all sizes within each station.
Results
On average, a panel of 10 experts or more yielded cut scores with confidence more than or equal to 90% and 15 experts yielded cut scores with confidence more than or equal to 95%. No significant differences in cut scores associated with panel size were identified for panels of 5 or more experts.
Conclusion
The EZ method was found to be valid and feasible. Less than an hour was required for 12 panelists to assess 12 OSCE stations. Calculating the cut scores required only basic statistical skills.
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Effects of a simulation-based blended training model on nurses’ treatment decision-related knowledge about oral cancer in Taiwan: a pilot survey
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Chia-Chang Huang, Shiau-Shian Huang, Ying-Ying Yang, Shou-Yen Kao
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J Educ Eval Health Prof. 2021;18:10. Published online May 25, 2021
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DOI: https://doi.org/10.3352/jeehp.2021.18.10
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Abstract
PDFSupplementary Material
- The present study aimed to evaluate the effects of virtual reality (VR) simulations combined with bedside assignments on nurses’ self-efficacy in providing pre-treatment educational services. Between March 2019 and November 2020, we conducted a study of VR educational materials that were developed to cover information about the treatment of oral cancers. The effects of the VR simulation, the thinking-path tracking map method, and bedside assignments on the nurses’ treatment decision-related knowledge were evaluated in a ward for oral cancer patients at Taipei Veterans General Hospital, Taipei, Taiwan. The blended training model significantly increased nurses’ familiarity (P<0.01) and confidence (P<0.03) regarding their knowledge of treatments and treatment decision-related knowledge. This model also significantly increased their confidence in their skills in bedside pre-treatment education for admitted oral cancer patients (P<0.002). Oral cancer-specific VR materials enhanced the effectiveness of skills training among nurses in the oral cancer ward.
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Citations
Citations to this article as recorded by  - The use of simulation-based education in cancer care: a scoping review
Amina Silva, Kylie Teggart, Corey Heerschap, Jacqueline Galica, Kevin Woo, Marian Luctkar-Flude
International Journal of Healthcare Simulation.2023;[Epub] CrossRef - Application of computer-based testing in the Korean Medical Licensing Examination, the emergence of the metaverse in medical education, journal metrics and statistics, and appreciation to reviewers and volunteers
Sun Huh
Journal of Educational Evaluation for Health Professions.2022; 19: 2. CrossRef - Assessing the Financial Sustainability of High-Fidelity and Virtual Reality Simulation for Nursing Education
Michael D. Bumbach, Beth A. Culross, Santanu K. Datta
CIN: Computers, Informatics, Nursing.2022; 40(9): 615. CrossRef
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