Programplaner og emneplaner - Student
PHVIT9510 Concept and Theory Development in Health Sciences Course description
- Course name in Norwegian
- Begreps- og teoriutvikling i helsevitenskap
- Study programme
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PhD Programme in Health SciencesPh.D. programme in Health Sciences - Individual Courses
- Weight
- 5.0 ECTS
- Year of study
- 2019/2020
- Curriculum
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FALL 2019
- Schedule
- Programme description
- Course history
-
Introduction
This course covers research into key phenomena originating from human experience of health and illness. Such phenomena can be perceptions of quality of life, hope, coping, pain, body, dignity, and suffering, and serve as the basis for developing concepts and theories in the health sciences. The course covers different methods for concept and theory development; for example, the hybrid model of concept development, semantic concept analysis, Walker and Avant's model for theory development, and grounded theory. The methods are applied to selected phenomena, concepts, and theories.
Required preliminary courses
None
Learning outcomes
On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:
Knowledge
The PhD candidate:
- is at the forefront of knowledge of methodologies related to the development of concepts and theories
- has an in-depth understanding of the relationship between human experience, concepts, and theories in a health science context
Skills
The PhD candidate can:
- develop a conceptual and/or theoretical framework related to his or her own research
- identify relevant clinical phenomena in order to develop concepts and theories
- analyse and interpret research findings related to concept and theory development
- address complex scientific issues and challenge established knowledge and practice in concept and theory development
General competence
The PhD candidate can:
- argue in favour of particular methodologies in concept and theory development on the basis of scientific theory
- participate in discussions on concept and theory development
Teaching and learning methods
Work and teaching methods consist of lectures, seminars, and self-study. The outcomes of the seminars are presented and discussed in plenary sessions.
Course requirements
None
Assessment
Candidates must write an essay based on a concept and/or theory of their choice and apply principles from the methods of concept and theory development presented in the course. The essay must consist of up to 5,000 words and must be submitted no more than 2 weeks after the end of the course.
Permitted exam materials and equipment
All
Grading scale
This course will present complex systems (cellular automata, networks, and agent-based) modelling and programming through state-of-the-art artificial intelligence methods that take inspiration from biology (sub-symbolic and bio-inspired AI methods), such as evolutionary algorithms, neuro-evolution, artificial development, swarm intelligence, evolutionary and swarm robotics.
Examiners
No formal requirements over and above the admission requirements.
Admission requirements
On successful completion of the course, students will get both theoretical and practical experience within complex systems and bio-inspired/sub-symbolic AI methods. In particular, students should have the following outcomes defined in terms of knowledge, skills and general competence:
Knowledge
The student:
- Has a deep understanding of complex systems modelling and analysis
- Has advanced knowledge in sub-symbolic and bio-inspired AI methods
- Has a clear understanding of key concepts in AI such as emergence, adaptation, evolution.
- Can relate concepts in artificial intelligence and biological intelligence
Skills
The student:
- Can model and analyse complex systems using cellular automata, networks and agent- based models
- Can program complex systems using bio-inspired AI methods
- Can design and implement evolutionary and swarm robotic systems
General Competence
The student:
- Has theoretical and practical understanding of complex and biologically-inspired AI methods and evolutionary robotics methods
- Can understand and discuss relevance, strength and limitations of complex and biologically inspired systems
- Is able to work in relevant research projects