Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Master's Degree Dissertation I
Code: 44724Credits: 3
| Degree programme | Type | Course |
|---|---|---|
| General Health Psychology | OB | 1 |
Contact lecturer
- Name :
- Juan Vicente Luciano Devis
- Email :
- juanvicente.luciano@uab.cat
Teaching staff
- Antoni Sanz Ruíz
- Juan Vicente Luciano Devis
- Sonia Lorente Sànchez
Group languages
You can consult this information at the end of the document.
Prerequisites
There are no prerequisites.
Objectives
The philosophy of the Master Dissertation (MD) is to confront the student with a real research problem related to general health psychology, either by joining a research team to contribute to an already started line, or by identifying, with the help from the tutor/s, a health need, problem or lack of interest. To achieve this purpose, the TFM places the student in a position to deploy and combine all the skills developed so far in order to 1) solve the problem or question addressed and thereby 2) demonstrate that they possess the capacity for which the MUPGS will accredit you: that of using your knowledge and skills to solve real problems.
During the TFM I subject, the student will, in agreement with the person(s) who tutor their work, carry out the feasibility report of their MD, including the search for information that allows the definition of research problem, a preliminary version of the MD introduction and the choice of methodology. This report becomes the master's dissertation planning, which will be executed later in the subject DM-II.
Learning outcomes
- Continue the learning process, to a large extent autonomously
- Use acquired knowledge as a basis for originality in the application of ideas, often in a research context.
- Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
- Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
- Carry out a multidimensional-multilevel analysis of health-illness processes.
- Recognise and describe the factors determining health from a biopsychosocial perspective.
- Integrate perspectives and approaches in health-illness processes.
- Write the master's dissertation in accordance with regulations on ethical professional practice and research in the field.
- Plan and carry out a systematic documentary search that unearths scientific evidence.
- Use documentary and bibliographic databases to obtain information relevant to the objective of the master's dissertation and to professional practice.
- Know and apply quality criteria to the selected documentary sources and clinical material.
- Justify the sources of clinical information used (evaluation tests, clinical guidelines, informants, programmes of treatment, etc.) for the master's dissertation.
- Formulate research questions, objectives and hypotheses appropriately to solve problems in the context of the master's dissertation.
Contents
The student can choose one of the following options depending on his/her interests and the interests or proposals allowed by the tutor supervising their work:
A. Systematic Review following PRISMA Guidelines
B. Empirical Research
C. Development of a new tool for Health Psychology practice: therapy programmes, health guidelines, e-health project, assessment procedures....
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Developing MD feasibility report | 68.5 | 2.74 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
| Follow-up sessions with the academic tutor | 6 | 0.24 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
The Master's thesis I has some characteristics that distinguish it from other subjects:
1) It is eminently autonomous, that is to say, the student must undertake most of the hours involved in his master's thesis project.
2) The master's thesis teaching is non-directed and is based on tutorial action. The student does not attend class but is assisted by his/her tutor(s) and has contact with the rest of the students through the virtual classroom.
3) To do this, the student is expected to deploy, develop and integrate a large number of previously developed skills (during the degree, the rest of the master subjects and contact with tutors).
4) The master's thesis must be the proof that the student can integrate what he has learned and put it to the service of addressing a complex reality, solve problems, and show that he can continue with his self-taught training.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Delivery of the DM feasibility report | 100 | 0.5 | 0.02 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
- The evaluation of MD-I is based on a single evidence, consisting of the elaboration of the feasibility report of the DM (EV1). This evidence will be presented in the second half of June 2026 and it will be delivered through the space enabled in the virtual classroom.
- To pass the MD-I, the student must achieve a minimum score of 5.0.
NOTE: Rounding up to the next integer will be applied when such rounding allows for a qualitative change in the grade and the grade is only one tenth away from that change. Pass: 4.85 → 4.9 = 5.0; merit: 6.85 → 6.9 = 7.0; excellent: 8.85 → 8.9 = 9.0.
- Link to the avaluation rules of the Faculty (https://www.uab.cat/web/estudiar/graus/graus/avaluacions-1345722525858.html)
- This subject does not provide for the single assessment system.
- This subject does not include a synthesis test for second-year students.
Feedback
| Type of feedback | EV and TYPE | WEEK |
| Written (rubric) | EV1: Feasibility report | W19/21 |
| Digital tool |
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| In the classroom |
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| Tutorial |
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Not assessable: If the student does not submit EV1 in the corresponding space on the virtual classroom by the deadline of 22/06/2026, it will be considered \"not assessable\".
Restricted Use of AI
By default, in the TFM-I course, the use of Artificial Intelligence (AI) technologies is permitted solely as a support tool for tasks such as information retrieval, language correction, style improvement, or text translation. In all cases, the use of AI must be previously agreed upon with the supervisor(s). In addition, the student must clearly identify which parts of the work were generated with the help of such technology, specify the tools used, and include a critical reflection on how these tools influenced the work process and the final outcome. Lack of transparency in the use of AI in the only assessable activity —the feasibility report— will be considered a breach of academic integrity and may result in a partial or full penalty on the activity’s grade, or even more serious sanctions depending on the severity of the case.
Software
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TFMmRD) Treball de fi de màster (màster RD) | 1 | Catalan | second semester | morning-mixed |