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    Developing a web-based multiple-choice question item bank
    (Wiley-Blackwell, 2010) Caliskan, S. Ayhan; Durak, H. Ibrahim; Torun, S. Elif; Karabilgin, O. Surel
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    Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study
    (Bmc, 2021) Karaca, Ozan; Caliskan, S. Ayhan; Demir, Kadir
    BackgroundIt is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. in this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine.MethodsTo define medical students' required competencies on AI, a diverse set of experts' opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied.ResultsA total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach's alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (chi (2)/df=3.81, RMSEA=0.094, SRMR=0.057, CFI=0.938, and NNFI (TLI)=0.928). These values showed that the four-factor model has construct validity.ConclusionsThe newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications.Medical schools may follow 'a physician training perspective that is compatible with AI in medicine' to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants' end-course perceived readiness opportunities.
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    Medical students' opinions on career planning course: evaluations of the relationship between course and faculty attributes and student characteristics
    (Walter De Gruyter Gmbh, 2022) Caliskan, S. Ayhan; Durmaz, Seyfi; Akcicek, Selahattin Fehmi; Bati, Ayse Hilal; Kalyoncu, Ebru; Karabilgin Ozturkcu, Ozlem Surel; Altun Koroglu, Ozge
    Objectives The Presidency of the Republic of Turkey Human Resources Office has stated that a Career Planning Course (CPC) should be implemented in higher education curricula by 2020. An institutional CPC consisting of 10-online sessions was designed and implemented in at Ege University Faculty of Medicine (EUFM) curriculum. This study reports the design, implementation, and evaluation of this new CPC at EUFM. Methods A descriptive, cross-sectional research design was used in this study. An online questionnaire (n=253) focusing mainly on Kirkpatrick-model Level 1 was administered to gather students' feedback on CPC as well as perceptions regarding the concept of Career Planning. Percentage distributions were used for categorical variables and mean +/- standard deviation calculations were used for numerical variables. Student's t-test was used to compare students' characteristics with career planning and important factors, and Pearson correlation test was used to evaluate competency areas with the mean scores of important factors in career planning. Statistical significance level was accepted as pResults The perception of the term career planning revealed 12 themes of which life was the most common (140/245; 57.1%), followed by professional life (102/245; 41.6%). Participants' total satisfaction rate was found 65.6% (Mean=39.36 +/- 14.88) for the course. Students were most satisfied with the goals and content appropriateness (7.77 +/- 3.08). The stimulating and motivating attribute of the CPC was the least satisfactory item. Conclusions An evaluation report, based on students' feedback, was shared with the faculty members involved in education via an interactive web page. The survey not only benefitted course educators but also helped students to reflect on the course content. CPC can help students to address their strengths and weaknesses and hopefully to take supportive initiatives at the beginning of their career.
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    The publication of health sciences theses in Turkey: A study of Ege University
    (Wiley, 2020) Karaca, Ozan; Caliskan, S. Ayhan; Durak, Halil Ibrahim
    This study aimed to determine the academic publication rate of health sciences graduate theses, as well as the factors that influence researchers to publish. the study took place in Ege University Institute of Health Sciences, Turkey, and used a correlated research model to analyse both qualitative and quantitative data. We obtained data from 159 graduate students selected from 437 who graduated between 1 September 2009 and 31 August 2014. From this group, 76 of the theses were published as 141 items. of these, 93 (66%) were journal articles, of which 51 (36.2%) were published in journals indexed by Science Citation Index (SCI)/SCI-Expanded/Emerging Sources Citation Index (ESCI). in the multiple regression analysis, we found that employment in a university increased the probability of producing publications from theses by 8.54 times (odds ratio [OR], 95% confidence interval [CI] = 3.954-18.440) and that encouragement from a supervisor increased the same by 2.79 times (OR, 95% CI = 1.021-7.640). Reasons for not publishing their theses were classified into 11 items, of which thematic analysis showed that the most prominent reasons were lack of time (n= 18, 24.3%) and workload (n= 15, 20.2%). This suggests that interventions targeting these two factors may result in increased publication of theses.