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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Advances in Medical Education and Professionalism
- نوع مقاله: Journal Article
- کلمات کلیدی: Medical students,COVID-19,Pandemic,Medical education,Doctors’ assistants
- چکیده:
- چکیده انگلیسی: Introduction: The COVID-19 Pandemic brought clinical placements to a halt for many UK medical students. A University Hospitals Trust offered clinical phase students the opportunity to support the National Health Service (NHS) in newly defined roles as Doctors’ Assistants (DAs). This study evaluates the experience of students working in a single NHS Trust. To our knowledge, this
is the first report of medical students’ perspectives on taking up a novel clinical role in the UK.
Methods: An anonymised novel electronic survey was sent to all 40 DAs across a single University Hospitals Trust via email to determine student perceptions of several aspects of the role, including its value to learning and development, impact on wellbeing, and benefit to the clinical environment. A formal statistical analysis was not required.
Results: Of the total cohort participating in the programme, 32 DAs responded (80% response rate). The experience was considered valuable to multiple aspects of learning and development, particularly familiarisation with the role of a Foundation doctor. Levels of confidence in training and support
were high, and most DAs felt valued as part of the clinical team, and experienced no mental health issues resulting from their role. 53% of the participants felt their work was necessary or valuable
to the team, and all reported a positive experience overall.
Conclusion: A new role allowed medical students to effectively provide clinical assistance during the COVID-19 pandemic. This provided immediate support to clinical teams as well as learning
opportunities for the participants without detriment to their mental well-being, and could be a model for effective retention of medical students in clinical environments in the face of resurgence of
COVID-19.- انتشار مقاله: 24-05-1399
- نویسندگان: DANIELLE M LAVENDER,ANDREW P DEKKER,AMOL A TAMBE
- مشاهده
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: Artificial Neural Networks,Machine Learning,Breast cancer risk,missing values,inaccurate data
- چکیده:
- چکیده انگلیسی: Purpose: To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information. Data and methods: Open data for this study was obtained from the BCSC Data Resource (http://breastscreening.cancer.gov/). We conducted two ablation-type experiments to compare the robustness of different classifiers where we randomly switched known information to missing with a missing probability of pm in one experiment, and randomly corrupted the existing information with a probability of pc in another experiment. We considered three prominent machine-learning classifiers such as Logistic regression (LR), Random Forests (RF) and a custom Neural Network (NN) architecture and compared their degradation of discrimination performance as a function of increasing probability of missing or inaccurate data. Results: LR, RF and custom NN resulted in an Area Under Curve (AUC) of 0.645, 0.643 and 0.649, respectively, on a test set with 500,000 total observations. When we manipulated the data by varying probabilities pm and pc from 0 to 1, NN resulted in better performance in terms of AUC compared to RF and LR as long as less than half the data was missing/inaccurate (that is, for values of pm < 0.5 and pc < 0.5). However, for missing (pm) or corruption (pc) probabilities above 0.5, LR gave similar performance as the custom NN. RF resulted in overall poorer performance when the data had additional missing or incorrect entries. Conclusion: In cases where the input information is missing or inaccurate, our experiments show that the proposed custom NN provides reliable risk estimates in medical datasets like BCSC. These results are particularly important in health care applications where not every attribute of the individual participant might be available.
- انتشار مقاله: 07-02-1399
- نویسندگان: Siva Teja Kakileti,Geetha Manjunath,Andre Dekker,Leonard Wee
- مشاهده