Postgraduate Course: Clinical Project (STEM11010)
Course Outline
| School | Deanery of Clinical Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | In this course you will have multiple opportunities to explore real clinical data gathered (and anonymised) through the Ann Rowling Regenerative Neurology Clinic. Examples could be a clinical audit or systematically reviewing data collected from hundreds of patients from clinical trials for neurodegenerative diseases. You will get to analyse these datasets and gain feedback prior to assessments. This course will help with your understating of the background to a clinical dataset, its relationship to the literature and how it may be utilised, mined and/or expanded. |
| Course description |
This course will allow students to experience and work with real clinical datasets as well as learning how they are generated and the ethical and regulatory rules that govern them.
This course will start by laying the groundwork of clinical data gathering as well as ethical and regulatory rules before letting students decide upon several clinical datasets that they will then need to mine and present their results. The assessments will be designed around specific pillars of the datasets and for example may include presenting the background and need for the dataset, followed by their results and finally a conclusion on what further data needs to be collected to improve and widen their results.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2021/22, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
| Course Start Date |
20/09/2021 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
196 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
This course will be 100% assessed by coursework. Components of this assessment will be generated from a selection of the following: group presentation, ongoing assessment of discussion boards, analysis of scientific data or an essay question based around the key topics and learning outcomes to allow students to demonstrate their critical understanding and significant range of knowledge. Therefore a potential example of these assessments could be:
1. 40% - dataset audit (written 1500-2000 words)
students will be given access to a clinical dataset and expected to analyse and present their results and draw conclusions.
2. 60% - Essay (2000-3000 words)
Students will be presented with a finished clinical dataset and explanation but will need to write the background and conclusions and then also look forward to how the dataset could be improved/widened and weaknesses etc based on their knowledge and the literature
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| Feedback |
Students will have feedback provided in multiple ways including:
2 specific points in the course for formative feedback prior to summative assessments. These formative feedback points will allow students to gain feedback on their data analysis techniques and writing capabilities as well as demonstrating their understanding of the literature and relevant fields of clinical research.
Furthermore discussion boards will be used throughout the course and present multiple opportunities for students to gain feedback and input to their work and understanding.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of clinical datasets and how to analyse them
- Evaluate, criticise and appraise the literature around this topic
- Demonstrate their advancement in the basic research skills vital for clinical data understanding and displayed evidence-based practice and the application of theory to clinical data sets
- Communicate and engage with the course¿s concepts and principles with others outwith their own field
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Additional Information
| Graduate Attributes and Skills |
See learning outcomes below as well but specifically:
1. Due to working independently students must show a high degree of autonomy and time management skills to complete this course
2. Students will need to display a significant individual drive and determination to engage with this course and remain focused
3. Students will need to not only critically assess their own data with respect to drawing conclusions but this will also allow students to gain an understanding of real-life clinical gathering and how to mine that data
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| Keywords | Regeneration,Neurodegenerative diseases,translation,neurology,clinical trials,data handling |
Contacts
| Course organiser | Dr David Hampton
Tel: (0131) 242 9421
Email: David.Hampton@ed.ac.uk |
Course secretary | Ms Catherine Renton
Tel: (0131 6)50 6410
Email: Catherine.Renton@ed.ac.uk |
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