SYBASPRA4 Clinical Studies, Mental Health and Substance Abuse Course description

Passed first year of the programme or equivalent.

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

Knowledge: The student has knowledge of

• clinical assessment processes in nursing aimed at attending to the patient's basic needs and resources in mental health care, including therapeutic treatment activities and milieu therapy
• different theoretical perspectives on mental health disorders and mental health work: the significance of relationships, professional ethics, aesthetics and dignity, and integrity in dealings with patients and next of kin
• coping strategies and recovery processes (patient perspective, user participation and empowerment)
• mental health and psychosocial problems among refugees and immigrants: trauma, loss, grief and identity
• mental health in a historical perspective
• the relationship between substance abuse and mental health disorders
• how mental health disorders such as psychoses, personality disorders, anxiety and mood disorders affect the patient's basic needs, as well as special circumstances relating to old age psychiatry
• pharmacology and drug administration related to relevant diseases
• compulsory provisions set out in health legislation that apply to the municipal and specialist health services

Skills: The student

• is capable of observing, assessing and providing nursing care to patients with mental health disorders and reflecting on their own professional practice in relation to those with mental health disorders
• masters relevant drug administration
• is capable of taking active part in interdisciplinary cooperation
• is capable of using theory on personality psychological factors, cognitive functions, ego functions, identity, self-image and moods
• is capable of using theory on therapeutic communication and cooperation principles and theory on facilitating the therapeutic climate
• is capable of using relevant results from research and development work in dealings with and treatment of people with mental health disorders

Competence: The student is capable of

• planning and carrying out nursing interventions relating to patients receiving mental health care and substance abuse treatment
• sharing opinions that can contribute to professional development

Clinical training, lectures, group work, seminars, written assignments, logs, supervision and self-study.

Students will firstly take a preparatory course lasting approx. two weeks, before embarking on eight weeks' clinical training with direct patient contact.

All clinical training have different compulsory activities and tasks in the implementation

The assessment is based on the learning outcomes for the course, the student's specification of the learning outcomes and the formative assessment made of the student during the clinical training period.

Students' clinical training can only be assessed if their attendance is sufficiently high.

• Less than 10% absence: The student can complete the clinical training course as normal.
• 10-20% absence: If possible, the student can make up for the clinical training missed. This must be agreed with the clinical training supervisor and the supervisor at the university.
• More than 20% absence: The student must retake the whole clinical training course.

Students are responsible for obtaining a copy of their assessment form from the supervisor. The form must be presented to the new supervisor in the next clinical training period.

Not relevant.

Data analytics provides a set of both qualitative and quantitative techniques to analyze data in order to convert information into useful knowledge.

The last decade or so has seen sizable decreases on costs to gather, store, and process data, creating a fertile environment for the use of empirical (data analytics) approaches to problem solving based on big-data. The range of real-word problems that can be solved is wide: This course is aimed at teaching students about a set of tools and techniques that are state-of-the-art and commonly used for data analytics in the industry. Examples and practical exercises are geared towards demonstrating real-world use cases and make students proficient in using these tools as well as understanding the theory behind them.

It is recommended to have completed one full year of university studies (60 ECTS) before the program starts. It is also recommended that students have basic knowledge of statistics and programming.

One half year of university studies (30 ECTS), in addition to the international summer school's general requirement. The requirement needs to be met by 1 March.

After completing this course the student should have the following learning outcome:

Knowledge

On successful completion of this course the student has leading knowledge of:

• the potential of data analytics for solving different real-life problems
• the core principles of data analytics (machine learning, statistics)
• the main components of data analytics from infrastructure/technical perspectives
• the visualization techniques to understand and/or communicate data and findings
• programming languages applicable to data analytics including, for example, SQL, Python, Matlab, R, or SPSS

Skills

On successful completion of this course the student has a progressive ability to:

• apply data analytics techniques to formulate a hypothesis, collect data, analyze it and reach conclusions about the data
• critically draw conclusions from different sources of data
• plan and design a real-world data analytics application
• analyze real-world data by building an app with a real-world application
• clearly identify and define a problem and craft a solution using data analytics
• construct a data analytics solution from a set of general requirements such as organizational goals or user scenarios.
• synthesize primary and secondary data sources the ability to accurately pinpoint trends, correlations and patterns in complex data sets
• identify new opportunities for organizational change including process improvements, cost reduction or efficiency improvements.

General Competence

On successful completion of this course the student is proficient and can master:

• data analytics principles
• methods/tools for data analytics and data visualization
• using data analytics to solve real-world problems
• data analytics visualizations in presentations