PARAPRA2AB Clinical Studies 2 Course description

The courses PARAPRA1 and PARAPRA2AB comprise supervised clinical training, mainly in the ambulance service. The first part of PARAPRA2AB (part A) makes up six weeks during the fifth semester. The final part (part B) makes up nine weeks during the sixth semester. The learning outcomes from all the previous courses are updated, integrated, tested in clinical training and form the basis for the development of new theoretical and practical competence.

The learning outcomes are very similar for the two clinical training courses. They are achieved by participation in a variety of ambulance call-outs to patients with different injuries and diseases. The student is expected to show progress, take responsibility and, to an increasing extent, make independent assessments in prehospital work.

This course will give the student insight into the different parts that comprise the internet's architecture and how one can monitor, assess and characterise them. This involves a diverse set of topics that includes but is not limited to routing and addressing, content distribution, data centre networks, key services and application such as DNS and web and mobile broadband. The course will focus particularly on quantification of the robustness and reliability of the internet's architecture and services. Furthermore, the course will draw upon new advancments in the fields of machine learning and network science to extend and expand the toolset available for anlayzing Internet measurements.

The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• is capable of using theory to give grounds for his/her clinical practice
• is capable of explaining how advanced lifesaving diagnosis and treatment can affect the planning and performance of clinical ambulance activities
• is capable of explaining and reflecting on the use of all available equipment
• is capable of asking questions based on theoretical knowledge and experience gained from practical training
• is capable of explaining and reflecting on the choice of systems for patient record documentation
• is capable of explaining and reflecting on the use of tools and methods in lifesaving measures in clinical ambulance activities
• is capable of reflecting on the risk factors in prehospital pharmacological treatment
• has broad theoretical knowledge of tools and methods used in clinical ambulance activities
• has knowledge of research in prehospital emergency medicine
• is capable of keeping up to date with research in key areas in clinical ambulance activities

Skills

Under minimal supervision, the student can

• recognise signs and symptoms of possible serious illness or injury
• use standardised examinations and treatment guidelines
• plan, assess and carry out treatment measures adapted to the individual patient's needs
• apply and supervise others in the use of all equipment available in the ambulance
• assess the need to obtain assistance or refer the patient
• write ambulance patient records in accordance with the guidelines
• document in writing and evaluate his/her own clinical work in accordance with the practical training institution's procedures and guidelines
• apply and supervise others in the use of transfer techniques
• explore professional issues in a systematic, evidence-based and reflective manner
• drive an emergency vehicle

General competence

The student

• is capable of organising his/her own work and maintaining order and hygiene in clinical activities
• is capable of initiating patient treatment in order of priority in accordance with the applicable legislation, framework conditions and professional ethics guidelines
• demonstrates empathy, care, understanding and respect in relation to patients, next of kin and colleagues
• is capable of reflecting on interdisciplinary cooperation
• is capable of reporting relevant patient information to other cooperating health personnel and agencies within the applicable legal limits
• is capable of identifying his/her own knowledge needs, demonstrating insight in relation to his/her own learning needs and obtaining new knowledge and skills
• is capable of collaborating and showing humility in the context of assessing his/her own work performance
• is capable of giving constructive feedback and supervising others

Knowledge

On successful completion of the course, the student:

• has an overview of the different elements that comprise the architecture of today’s internet.
• has a good understanding about the approaches for conducting internet measurements and the latest advances in this field.
• be familiar of a broad set of tools that can help analyzing Internet measurments. Of a particular relevance here are tools that originate in other disciplines like Machine Learning and Statisitcal Physics. This will not only expand the available toolset but also increases the potential for interdisciplinory collaboration going forward.

Skills

On successful completion of the course, the student can:

• plan and carry out state-of-the-art measurement tasks
• can formulate research questions on the robustness and performance of operational networks, and design measurements for evaluating these questions.
• will have a general practical understanding of how different parts of the internet's architecture interplay to offer a performant end-to-end service.

General competence

On successful completion of the course, the student can:

• participate in debates and present aspects of his/her expertise in a way that promotes such discussions.
• drive innovation

Module 1 will take the form of lectures. Module 2 will take the form of lab and homework assignments. Module 3 will take the form of seminars. In module 3, the student will present a case to the other students. We will also invite guest lecturers from research groups that focuses on machine learning and network science to introduce the students to potential tools and analysis methods.

Practical training

The students will participate in lab experiments to explore how once can measure various aspects of internet's robustness and performance. The students will write a summary of one of the tools that were introduced in the lab and discuss its benefits and limitations.

None.

All aids are permitted.

Pass or fail.