VPSYPRA11 Relationship and Care for People with Mental Complaints Course description

Language of instruction: Norwegian

The relationship between mental healthcare workers, users/patients and next of kin is very important when promoting health and providing care to people with mental health complaints. Practising mental health care requires a high level of relational skills, including ethical awareness and professional expertise.

The purpose of the course is to give the students a practical introduction to and training in relational skills when interacting with people with mental health complaints. Students will take practical training that will provide a holistic understanding of the relationship between the framework conditions for service provision and the practice of mental health care. Two different forms of practical training form part of the course. The course concludes with the submission of an in-depth assignment with emphasis on user-related challenges and/or phenomena related to practical mental health care.

The clinical training will normally be carried out in places affiliated with OsloMet – Oslo Metropolitan University through cooperation agreements. The student may apply to complete the clinical training elsewhere. If the application is granted, the students is responsible for covering any expenses relating to this. The clinical training cannot be completed in the students' own workplace, i.e. the student's own unit, ward or similar.

Master's Degree Programme in Health Sciences - specialisation in Mental Healthcare and Advanced Programme in Mental Health Care is carried out jointly.

Admission to the programme.

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

Knowledge

The student

• can understand human reactions after challenging life experiences and crises, and the significance of these based on recognised mental health care theory
• can described the user/patient and next of kin’s legal rights, and health personnel and the health service’s obligations
• can apply relational and care theory to analyse how attitudes and communication affect relationships and care
• can explain existential dimensions related to hope and meaning in mental health care;

Skills

The student

• can apply relational knowledge to see things in light of the user/patient and next of kin’s life situation
• can facilitate targeted communication and cooperation with users/patients and next of kin
• can analyse the connection between treatment models and work methods to promote the mental health of users/patients
• can assess and discuss professional, legal and ethical aspects with users/patients, next of kin and their collaborative partners

General competence

The student

• can actively contribute to patient-oriented mental health care adapted to the individual, regardless of background, culture and nationality
• can support, guide and cooperate with users/patients and next of kin to enable targeted promotion of health and quality of life
• can analyse and adjust their own professional practice based on their preconceptions and new knowledge
• can contribute to expanding knowledge, insight and openness to promote mental health
• can actively contribute to more openness and less stigma associated with mental health complaints

The course will use varied, student-active work methods. Work and teaching methods comprise project-oriented training, supervised practical training with adapted learning situations, simulation, written and oral assignments and self-study.

The project-oriented training lasts approximately three weeks;and will focus on the organisation of the mental health services from an improvement perspective. Students will work on the topic by completing a project in which they obtain knowledge and experience from a professional practitioner in a relevant field of practice.

Practical training, where the student develops professional skills and relational competence. Practical training runs over eight 30-hour weeks, with 240 hours in total.

The following must have been approved in order for the student to take part 2 of the examination:

• presentation based on project-oriented practical training (individual or in groups). Scope of about 30 minutes with an opponent;
• individual reflection note from practical training. Scope: 700 words (+/- 10%).

Combined assessment:

Part 1) Assessed practical training The assessment is based on the learning outcomes for the course, the student's specification of the learning outcomes and the assessment made of the student during the practical training period.

Students’ practical training can only be assessed if their attendance is sufficiently high.

• The student must attend at least 90% of the scheduled time
• The student will be allowed to make up for absence of between 10 and 20% by agreement with the practical training supervisor and the supervisor from the university
• If a student’s absence exceeds 20%, he/she will fail the practical training*
• The attendance requirement cannot be deviated from due to illness or for other reasons

*If illness prevents the student from attending the practical training so that their absence exceeds the permitted amount, the student is normally required to present a valid medical certificate for all days of absence in excess of 10% of the scheduled time in order for the absence to be deemed valid. The student’s attempt at taking the practical training will then not count.

Part 2) An individual project examination in the form of an in-depth assignment

• Scope: 5,000 words (+/- 20%)
• Carried out in the course of the practical training period

Resit examination: If the student is awarded the grade F (fails the written assignment),;he/she will be given one (1) opportunity to submit a reworked version.

This course provides a broad introduction to machine learning (ML), which includes supervised, unsupervised, and reinforcement learning, and deep learning (DL) that can be used in different application domains. Students will learn both theories and practices in ML and DL. Moreover, students will learn from studying, presenting, and discussing relevant research articles and expose themselves to research by doing a research project.

No formal requirements over and above the admission requirements.

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills, and general competence.

Knowledge

The student:

• is knowledgeable about supervised, unsupervised, reinforcement learning
• has a good understanding of the principles of state-of-the-art deep neural networks such as convolutional neural networks, sequential models (RNN, LSTM), Transformer, Generative models (Autoencoder, GAN), and reinforcement learning.
• has a good understanding of both theoretical and practical know-how required to use machine learning and deep learning methods effectively

Skills

The student can:

• build, train, test, and deploy machine learning and deep learning models
• analyze machine learning methods in regard to their performance and effectiveness
• use existing deep learning networks, improve and/or customize them to apply to new problems

General competence

The student:

• has both theoretical and practical understanding of machine learning and deep learning methods
• can discuss relevance, strength, and limitations of machine learning and deep learning in solving real-world problems
• can work on effectively relevant research projects

The course consists of lectures, assignments, group consultations, presentation seminars, and project work. In the seminars, students will read papers, present, and also actively participate in other presentations. This will facilitate research-oriented education in the field. Research projects will be aimed at cultivating the students towards good future researchers.