Background: The objective was to determine the utilization, frequency, characteristics, and standard-setting methods of progression assessments in pharmacy education.
Methods: A survey was sent to 139 United States schools/colleges of pharmacy having an identifiable assessment lead and students enrolled in the doctor of pharmacy program. The survey examined programs' use, frequency, and characteristics of progression assessments within their curriculum. Respondents also reported any changes made due to the COVID-19 pandemic and which, if any, would be maintained in future years. Analysis consisted of descriptive statistics and thematic coding. This research was deemed exempt by the university's institutional review board.
Results: Seventy-eight programs responded to the survey (response rate = 56%). Sixty-seven percent of programs administered at least one progression assessment in 2019-2020. There was some variability in assessment practice, including professional year(s) administered, course(s) involved, and content. Approximately 75% of programs used assessments to ensure student competency in the programs' learning outcomes and to identify individual student learning deficiencies. Diversity was seen in validity and reliability practices, and most programs used pre-determined cut scores without formal standard setting. Because of the pandemic, 75% of programs changed the assessment delivery mode and 20 programs planned to maintain at least one pandemic-related change in future iterations.
Conclusions: Most pharmacy programs utilize some type of progression assessment within their curriculum. While many schools administer progression assessments, there is little agreement on their purpose, development, and use. The pandemic changed the mode of delivery, which numerous programs will continue with in the future.
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http://dx.doi.org/10.1016/j.cptl.2023.04.003 | DOI Listing |
Diabet Med
January 2025
Universidad Científica del Sur, Lima, Peru.
Background And Aims: Impaired glucose intolerance (IGT) and impaired fasting glucose (IFG) are totally different. Lifestyle modification is effective in moving from prediabetes to normoglycaemia. There is a lack of information showing the effect of lifestyle modification according to each prediabetes and assessing its effect on the degree of reversibility to normoglycaemia and on cardiometabolic markers.
View Article and Find Full Text PDFJ Cheminform
January 2025
Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, University of Bonn, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany.
Analogue series (AS) are generated during compound optimization in medicinal chemistry and are the major source of structure-activity relationship (SAR) information. Pairs of active AS consisting of compounds with corresponding substituents and comparable potency progression represent SAR transfer events for the same target or across different targets. We report a new computational approach to systematically search for SAR transfer series that combines an AS alignment algorithm with context-depending similarity assessment based on vector embeddings adapted from natural language processing.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan, 430060, P. R. China.
With breast cancer being the most common tumor among women in the world today, it is also the leading cause of cancer-related deaths. Standard treatments include chemotherapy, surgery, endocrine therapy, and targeted therapy. However, the heterogeneity, drug resistance, and poor prognosis of breast cancer highlight an urgent need for further exploration of its underlying mechanisms.
View Article and Find Full Text PDFBioData Min
January 2025
Department of Computer Science, Hanyang University, Seoul, Republic of Korea.
Background: Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures.
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