Purpose: Despite the importance of selecting students whom are capable for medical education and to become a good doctor, not enough studies have been done in the category. This study focused on analysing the medical students' academic performance (grade point average, GPA) differences, flunk and dropout rates by admission types.
Methods: From 2004 to 2010, we gathered 369 Konyang University College of Medicine's students admission data and analyzed the differences between admission method and academic achievement, differences in failure and dropout rates. Analysis of variance (ANOVA), ordinary least square, and logistic regression were used.
Results: The rolling students showed higher academic achievement from year 1 to 3 than regular students (p < 0.01). Using admission type variable as control variable in multiple regression model similar results were shown. But unlike the results of ANOVA, GPA differences by admission types were shown not only in lower academic years but also in year 6 (p < 0.01). From the regression analysis of flunk and dropout rate by admission types, regular admission type students showed higher drop out rate than the rolling ones which demonstrates admission types gives significant effect on flunk or dropout rates in medical students (p < 0.01).
Conclusion: The rolling admissions type students tend to show lower flunk rate and dropout rates and perform better academically. This implies selecting students primarily by Korean College Scholastic Ability Test does not guarantee their academic success in medical education. Thus we suggest a more in-depth comprehensive method of selecting students that are appropriate to individual medical school's educational goal.
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http://dx.doi.org/10.3946/kjme.2013.25.3.201 | DOI Listing |
Asian Pac J Cancer Prev
January 2025
National School of Public Health, Rabat, Morocco.
Objective: This study aimed to investigate loss to follow-up (LFU) rates within breast and cervical cancer screening programs in Kenitra-Morocco, identifying contributing factors from both patient and healthcare worker perspectives to enhance care continuity.
Methods: The study was a non-experimental, mixed-methods design conducted in three-phases. We started by identifying LFU women and their characteristics from medical records, interviewing LFU women to ascertain reasons for discontinuation, and surveying healthcare workers for perceived determinants of LFU through semi-structured questionnaires.
Genetics
January 2025
Institute of Forest Sciences (ICIFOR-INIA), CSIC, Ctra. De la Coruña km 7.5, 28040 Madrid, Spain.
We present a new hierarchical Bayesian method using multilocus genotypes to estimate recent seed and pollen migration rates in a spatially explicit framework that incorporates distance effects separately for each type of dispersal. The method additionally estimates population allelic frequencies, population divergence values, individual inbreeding coefficients, individual maternal and paternal ancestries, and allelic dropout rates. We conduct a numerical simulation analysis that indicates that the method can provide reliable estimates of seed and pollen migration rates and allow accurate inference of spatial effects on migration, at affordable sample sizes (25-50 individuals/population) when population genetic divergence is not low (FST≥0.
View Article and Find Full Text PDFCJC Open
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Background: Supervised exercise programs improve walking impairment and quality of life (QoL) in patients with peripheral artery disease (PAD). However, such programs are underutilized, due to their limited accessibility. A feasible and effective exercise program is needed.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
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