MAJO4100 Sexual and Reproductive Health and Rights Course description

The focus of this course is human sexual and reproductive health and rights in a life cycle course perspective, founded on knowledge-based practice. Adolescents and young people’s sexual and reproductive health are emphasized, as an important part of health promoting practices to improve well-being and public health. Women’s health and challenges related to reproduction is an important part of this course.

The course is both directly and indirectly related to United Nations sustainability goals, including health and gender equality. As basis for exercising health promotion and relevant prevention as pertains to sexual and reproductive health, this course will emphasize counseling, communication and relevant pedagogics. Throughout this course the various topics will be problematized and discussed from a variety of global, national, historic and cultural perspectives focusing on values and human rights. The meetings with young people or adults may take place in intimate and unpredictable situations. It is important that the students are aware of, their own as well as others, perceptions of body, gender and sexual rights.

The student must have been admitted to the Master’s Degree Programme of midwifery.

Learning outcomes 

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 discuss women’s sexual and reproductive health in a life-cycle-course perspective
• can identify and analyze scientific and professional themes related to contraceptive self-determination, contraceptive methods, as well as contraceptive use.
• can assess and implement culturally sensitive approaches in relation to ethnic and gender diversity
• can analyze societal and cultural contexts of importance for human sexual and reproductive health and rights, including user perspectives, gender diversity, youth health and variations in sexual relationships

Skills  

The student 

• having acquired the relevant competence in order to obtaining requisition authority, can provide relevant requisition and administer contraceptives (Prescription rights are dependent upon Norwegian authorization as health personell)
• can plan and implement knowledge-based counselling for self-determined contraceptive use
• can carry out relevant procedures, including gynecological, in order to insert and remove intrauterine devices and implants

General competence  

The student 

• can analyze relevant scientific and professional themes related to sexual and reproductive health and rights and reflect on challenges for the midwifery profession´s independent and interdisciplinary practice
• has advanced understanding of the various user needs in order to apply knowledge of sexual and reproductive health and rights in a respectful and inclusive manner reflecting contextual insights into challenges of cross-cultural and global professional practice
• has an advanced understanding of the needs of different users and applies knowledge about sexual and reproductive health and rights in an open and inclusive manner

In this course, students will acquire an understanding of some of the most important principles of data science and cognitive technologies through project work and online resources. The students will be introduced to fundamental principles of machine learning, data science and artificial intelligence. The main focus will be on how to use these principles to solve industrial tasks by using open-source or other data science platforms. The goal is to provide the students with an introduction to machine learning, data science and artificial intelligence using online resources at the same time as the students solve an industrial problem in the form of a comprehensive project work.

In addition to the projects on offer, students can find their own projects within a relevant company, public organization or nonprofit. In this case, it is the student's responsibility to find a supervisor for the project within the external organization. All student-initiated projects must be approved by a supervisor at OsloMet before the start of the project. 

The workload for the project should correspond to two days a week over a twelve-week period during either the Spring or Autumn semester. If the project is completed in the summer, the workload should equal four days a week over a six-week period.

The elective course will only run if a sufficient number of students a registered.

No requirements over and above the admission requirements.

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 a basic technological understanding of the most important concepts in machine learning, data science and artificial intelligence
• has knowledge of the most important methods in machine learning, data science and artificial intelligence
• has knowledge of platforms that can be used to complete major data science projects (for instance IBM Watson’s cloud services)

Skills

The student:

• masters basic data science tools and can extract and visualise information from large quantities of data
• understands the workflow in bigger data science, artificial intelligence or machine learning projects
• is capable of using open-source and commercial tools that are used in industrial projects in the fields of data science, machine learning or artificial intelligence

General competence

The student:

• masters methods and tools used to develop and carry out projects in data science, machine learning or artificial intelligence
• is familiar with the different methods that are used to find the right tools to carry out data science projects
• has an overview of how to visualise and manipulate data and how to develop predictive methods for solving industry problems and other issues relevant to working life

Regular follow-up of the project work by a project supervisor.

The students will work in groups of three to five students to complete a project in data science, machine learning or artificial intelligence in cooperation with relevant external parties such as companies or public organisations.

The supervisor(s) can suggest suitable online courses in AI and data science that the students can take during the first few weeks of the course. The students are also encouraged to take other courses (https://cognitiveclass.ai) that will be useful in order to carry out the chosen project assignment. These courses may, among other things, deal with the following areas: Blockchain, the Internet of Things, Chat Bots, advanced use of data science, etc.

The course can be carried out individually by agreement with the course coordinator.

Projects are selected/distributed at the start of the semester. 

The following work requirements are mandatory and must be approved in order to prepare for the exam:

• A project outline that describes how the group will organise their work on the project.
• A standard learning agreement must be entered into between the project provider / supervisor and the student(s), and this must be approved by the course coordinator before the project can start.
• Three meeting minutes from supervisory meetings during the project period.
• An oral mid-term presentation, individual or in groups (max 5 students), 10 minutes + 5 minutes Q&A.

The deadlines for submitting the project outline and minutes of the meetings will be presented in the teaching plan, which is made available at the beginning of the semester.

Written project report (100% of the final grade).

A written project report delivered at the of the semester, individual or in groups (max 5 students), 4000 words +/-10 %.

For group projects, all members of the group receive the same grade. Under exceptional circumstances, individual grades can be assigned at the discretion of the project supervisor(s) and Head of Studies.

The exam result can be appealed.