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Teaching Competency Assessment Of PhD Students: Needs Based Assessment Study
Fatemeh Keshmiri, Atefeh Mosayebi ,
Volume 8, Issue 4 (11-2014)
Abstract

Background and Aim: In order to develop teaching competencies and prepare PhD candidates for future roles as faculty members of medical schools, the present study conducted to determine PhD candidates` educational needs and their skills concerning teaching competencies.

Materials and Methods: The present study was a descriptive- analytical and cross-sectional study that was conducted in year 2011. The Study population was PhD candidates who studied in Health School of Tehran University of Medical Sciences. In the present study a “Teaching Competencies Assessment” questionnaire was used that included 2 part s the demographic information and 16- items of educational competencies and needs. The validity and reliability of th e questionnaire was approved by alpha Cronbach’s coefficient (educational need 94% & educational skill 87%). Data analysis was conducted using SPSS.

Results: The result of the present study showed that teaching skills of PhD candidates were at “familiar without implementation capability” level. The lowest candidate s` skill was “Student Assessment” field. The candidates had educational needs in all 16 areas of teaching skill fields (3.85:5). “Lecture Presentation” (4.1:5), “logical structure of Presentation” (4.02:5) and “Motivating methods” (4.01:5) fields were the highest educational needs of PhD candidates.

Conclusion: The results of the present study confirmed the need for systematic planning in order to develop teaching competencies and prepare PhD candidates for teaching role in future.


Prediction of Sepsis Due to Acinetobacter Infection in Neonates Admitted to NICU
Niloofar Mohammadzadeh, Ziba Mosayebi, Hamid Beigy, Mohammad Shojaeinia,
Volume 14, Issue 6 (Feb & Mar 2021)
Abstract
Background and Aim: Sepsis is the most important disease in the first 28 days of life and one of the main causes of infant mortality in the intensive care unit. Its definitive diagnosis is possible by performing blood culture. Neonatal sepsis can be a clinical sign of nosocomial infections that are often resistant to antibiotics. Therefore, the purpose of this study was to create and evaluate a hospital sepsis prediction model and present its results to health care providers.
Materials and Methods: In this descriptive-applied study, the research population includes neonates admitted to the intensive care unit of Valiasr Hospital in Tehran and the research sample is the data of 4196 neonates admitted to this ward from 2016 to August, 2020. The initial features for creating a predictive model of sepsis were prepared by examining the relevant information sources and under the supervision of professors and officials of Valiasr Hospital's mother and fetus research center and its validity was confirmed by 5 neonatal professors of this hospital. In this research, machine learning algorithms have been used to create a sepsis prediction model.
Results: Accuracy and AUROC(area under the ROC curve) parameters were used to evaluate the generated models. The highest values of Accuracy and AUROC are related to Adaptive Boosting and random forest algorithms, respectively.
Conclusion: Learning curves show that using different training examples and more complex selection of combination features improves the performance of the models. Further research is needed to evaluate the clinical effectiveness of machine learning models in a trial.

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