This Guide describes the framework, contents, methodology and general norms of the subject, in accordance with the current curriculum. The final organization of the subject with regard to the number and size of groups, distribution in the calendar and dates of examinations, specific criteria of evaluation and review of exams, will be specified in each one of the Hospital Teaching Units (UUDDHH), who will explain it through their web pages and the first day of class of each subject, through the teachers responsible for the subject at the UUDHH.

For the 2019-2020 academic year, the professors designated by the Departments as responsible for the subject at the Faculty level and the UUDHH are:

Responsible department(s): Department of Genetics and Microbiology. Area of Microbiology Head of Faculty: Ferran Navarro Risueño

UUDHH Responsible

UD Vall d’Hebron: Juan Jose Gonzalez López

UD Germans Trias i Pujol: Pere-Joan Cardona Iglesias

UD Sant Pau: Ferran Navarro Risueño

UD Parc Taulí: Lorena Forqué Rodriguez

Methodology

The teaching methodology will consist of theoretical classes, laboratory work, specialized seminars and clinical cases presentation.

Theoretical classes will be taught in the form of master classes and constitute a basic knowledge base on which all the rest of the learning activity is supported.

The seminars will be held in groups of 20 students. In these seminars, the basic principles of the etiologic diagnosis of infectious diseases will be reviewed and discussed with students. These seminars are the theoretical basis for the correct realization of the laboratory work.

The laboratory work will be carried out in groups of 5-6 students. In these practices the students will develop different techniques tofamiliarize themselves with the methods of work of the Microbiology laboratory used for the etiologic diagnosis of infectious diseases.

Classroom practices are basedon the development of aclinical case on the part of the students, previous orientation on the part of the teachers about the content of the subject proposed, the way to develop it, as well as the objectives that have to be done. Achieve, through support tutorials. Afterwards the students will orally present in class the result of their work.

In the current exceptional circumstances, at the discretion of the teachers and also depending on the resources available and the public health situation, some of the theoretical classes, practicals and seminars organized by the Teaching Units may be taught either in person or virtually.

Face-to-face classes are considered works protected by intellectual property rights, which is why the recording of classes taught by the teaching staff by any means is not authorized, except in those cases in which the teaching staff expressly authorizes it in the corresponding session.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

CONTINOUS ASSESSMENT

The subject will be assessed through two partial exams.

CONTINUOUS ASSESSMENT

1. Evaluation of the theory (30% of the grade of each midterm)

1. Formats (one or more of the above):
2. Multiple-choice items
3. Restricted questions
4. Correspondence items

2. Evaluation of practices / seminars / classroom

1. Formats (one or more of the above):
2. Multiple-choice items
3. Restricted questions
4. Correspondence items
5. Troubleshooting
6. Oral defense of assignments
7. In the 1st part:
8. Internships → 15%
9. Evaluation of laboratory practices, right at the end of them (5%)
10. In the 2nd part:
11. Clinical case seminars + classroom practices → 20%

Requirement to partially pass and release subject matter

To release the subject of the midterm exams, a grade equal to or greater than 5 is required.

There will be a recovery assessment where the student will be examined for the parts not released in the partial exams. This recovery exam will have the same structure and relative weight of the different parts as the partial exams and will be averaged, if applicable, with the grade obtained in the freed partial. To pass the subject you will need to obtain an overall grade equal to or higher than 5 out of 10.

Attendance at classroom practices, laboratory practices, clinical case seminars and specialized seminars is mandatory and an essential condition to be evaluated.

Reviewing tests is considered a fundamental part of learning. With their examin front of them, students will hear what teachers expect from the various assessment activities. The day and time of the exam review will be announced together with the grades both on the Virtual Campus and on the notice board.

Students who do not take both the theoretical and practical assessment tests will be considered Not Assessed and will be recorded as such in the minutes, exhausting their rights to register for the subject.

Any irregularity in an assessment act (academic fraud, plagiarism or improper use of AI, unless this use is expressly authorized in the teaching guide), which may lead to a significant variation in the grade, means that this act will be graded with a 0. In the event that the teaching guide provides that in order to pass the subject it is an essential requirement to have obtained a minimum grade in this assessment act or that several irregularities occur in the assessment acts of the same subject, the final grade for this subject is 0. Apart from this, a disciplinary process may be initiated against the student who incurs any of these irregularities.

SINGLE ASSESSMENT

This subject is not subjected to a single assessment

Use of AI

In this subject, the use of Artificial Intelligence (AI) technologies is allowed as an integral part of the development of the work, provided that the final result reflects a significant contribution of the student in the analysis and personal reflection. The student will have to clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how they have influenced the process and the final result of the activity. Lack of transparency in the use of AI will be considered a lack of academic honesty and may lead to a penalty in the grade of the activity, or greater penalties in cases of seriousness.