Nursing Course O - Frosinone ASL Frosinone Umberto I

Activity type

Scienze propedeutiche

SSD

MED/01

Year

2nd year

Semester

2nd semester

CFU

2

Hours distribution

24 classroom hours

Lecturers

MARIA CRISTINA CESCHI

Activity type

Scienze interdisciplinari

SSD

INF/01

Year

2nd year

Semester

2nd semester

CFU

1

Hours distribution

12 classroom hours

Lecturers

MATTEO MANNI

Activity type

Scienze propedeutiche

SSD

MED/42

Year

2nd year

Semester

2nd semester

CFU

3

Hours distribution

36 classroom hours

Lecturers

Gianpiero Fabi

At the end of the course the student must know: Collection, representation and processing of epidemiological data. Prevailing morbidity and accidents, mortality, concept of lethality, endemic and epidemic distribution, generalities of epidemiological, descriptive, analytical, experimental studies, association measures and risk estimation, screening tests, national and regional guidelines, quality indicators, average hospital stay. Knowledge of hardware and software. Know and use the basic programs: word, excel. Knowledge of the e-mail service. Web search programs.

EDUCATIONAL OBJECTIVES: At the end of the course, students should be able to identify research approaches and the types of scientific studies in the epidemiological field. Understand the research methods applied to public health to define causal relationships between risk factors and health outcomes. Know the structure and main components of a computer, a computer network, and their different operating systems. Be aware of the legislation that coordinates data processing, especially in the healthcare field, and plan their activities in compliance with this training. Be able to use the most common word processing, data processing, and data presentation applications. Describe the basic concepts of computer science. Use appropriate computer language. Understand the usefulness of statistics and quantitative methods in the clinical/healthcare field. Know how to collect and present data sets through graphs and tables. Know how to use the simplest methods of descriptive statistics to describe qualitative and quantitative variables. Know the fundamentals of inferential statistics, interpret p-values, and confidence intervals. Critically and independently read the results of the scientific literature.
KNOWLEDGE AND UNDERSTANDING: Know and be able to interpret the basic elements of epidemiology, statistics, computer science, and English in healthcare.
APPLIED KNOWLEDGE AND UNDERSTANDING: Know how to plan care based on clinical data, caring principles, and scientific evidence in specialized, critical, and emergency settings. Know how to interpret and apply research findings to nursing practice. Implement nursing care responsibly,
considering ethical principles, regulatory provisions, and scientific evidence to ensure safety, continuity, and quality of care.
JUDGMENT ABILITY: Know how to evaluate the effectiveness of interventions adopted by monitoring outcomes. Know how to resolve care problems through a reflective, scientific, and collaborative approach.
COMMUNICATION SKILLS:
LEARNING ABILITY: Develop the ability to ask critical questions and
formulate hypotheses to improve clinical practice. Demonstrate the ability to consult
scientific sources and digital tools to independently update
themselves.

Basic knowledge of biology, chemistry, biostatistics and mathematics

The program's contents include:
EPIDEMIOLOGY
Definition of health and determinants of health/disease; Evolution of health definitions; The importance of measurement; Health indicators; Definition of epidemiology and fields of study of epidemiology; Scope of study of demography, census, age pyramid, fundamental population parameters; General mortality; Infant mortality; Measures in epidemiology: prevalence, incidence rate; Relationship between incidence and prevalence; Epidemiological risk measures; Association; Characteristics of causal association; Causal factors and risk factors; How to calculate the relative risk (RR); How to calculate the odds ratio (OR); Classification of epidemiological studies; Observational studies and experimental studies; Case-control studies (odds ratio); Cohort studies (relative risk); Clinical trials (outline, what does randomization mean); Definition of screening; Sensitivity, specificity, predictive values; Screening examples (e.g., neonatal, cancer)
COMPUTER SCIENCE
Computer Architecture (Hardware)
• Von Neumann Model: How the CPU, memory, and input/output interfaces interact;
• Central Processing Unit (CPU): Instruction execution cycle, clock frequency;
• Memory: Difference between volatile memory (RAM), mass storage (hard disk/SSD), and permanent memory (ROM/BIOS)
Information Representation
• Binary System: Concept of bits and bytes, how numbers and text are encoded (ASCII and Unicode codes).
• Digitization: Difference between analog and digital signals.
• Compression: Difference between lossy compression (e.g., MP3/JPG) and lossless compression (e.g., ZIP)
Software and Operating Systems
• Classification: System Software (OS) vs. Application Software
• OS Functions: Resource Management, File System, and User Interface (GUI)
• Licensing: Difference between Proprietary, Open Source, and Freeware Software
• Networks and the Internet
• Network Types: Difference between LAN (local) and WAN (global)
• Protocols: Basic Concepts of TCP/IP and How an IP Address Works
• The Web: Difference between the Internet (the infrastructure) and the World Wide Web (the service), how HTTP and DNS work
Security and privacy
• Malware: Viruses, Worms, Trojans, Ransomware
• Protection: Fundamentals of encryption, digital signatures, and the importance of backups
• Privacy: Regulatory information on digital data processing
STATISTICS
Introduction to Medical Statistics:
The logical phases of the research process
o Population and sample. Overview of probability sampling types and sample size
o Data collection and organization (data collection form, database, and dataset)
o Concept of statistical unit and variable
Descriptive statistics:
o Simple tables: frequency distributions for qualitative and quantitative variables
o Two-entry tables: distributions to describe the relationship between two qualitative
variables
o Graphical representations of frequency distributions
o Summary measures of position: mean, mode, median. Overview of quantiles
o Measures of dispersion: range, variance, standard deviation, coefficient of variation
Introduction to probability
o Axioms and theorems of probability
o Validity of a diagnostic test: sensitivity and specificity, positive and negative predictive value
The Normal Probability Distribution
Inferential Statistics
Hypothesis Testing
Comparison Tests for Quantitative Measures: The Student t-Test for Independent Groups
Comparison Tests for Qualitative Measures: The χ2 Test for Independent Samples

Teaching materials provided by instructors
 Lopalco P.L., Tozzi A.E. Easy Epidemiology. Rome: Il Pensiero Scientifico Editore; 2003
 Mezzini M. Basic Computer Science. 2nd ed. Pisa: Maggioli Editore; 2020
 Fowler J., Jarvis P., Chevannes M. Statistics for the Healthcare Professions. Naples: Edises; 2005
 Fain J.A., Vellone E. Nursing Research: Reading It, Understanding It, and Applying It. 2nd ed. Milan: McGraw-Hill; 2004
 Soliani L. Basic Statistics. Padua: Piccin; 2015

Module: STATISTICS
N/D
Module: COMPUTER SCIENCE
N/D
Module: EPIDEMIOLOGY
N/D

Theoretical lectures and practical exercises supported by PowerPoint presentations, videos, and other teaching materials. Attendance will be certified by signature sheets.

Students cannot be admitted to the exam if their overall attendance for the Integrated Course is less than 67%.

The oral exam is based on questions and exercises designed to assess the student's understanding of the topics covered in class. These exercises specifically test:
- clinical reasoning skills
- connections between the content of the various modules
- appropriateness of scientific language
- presentation skills
The final grade is determined by the examining committee based on an arithmetic mean obtained from the evaluations of the individual modules and ranges from a minimum of 18 to a maximum of 30 and 30 cum laude.

Example Epidemiology Questions:
1. Definition of epidemiology
2. Relationship between incidence and prevalence
Example Computer Science Questions
1. Terzoni S. Computer Science for the Nursing and Midwifery Professions. Milan: Hoepli; 2010
2. Principe R. Notes on Computer Science for the Healthcare Professions. Rome: Nuova Cultura; 2021
Example Statistics Questions
1. What is meant by non-probability convenience sampling?
2. What measures represent descriptive statistics?

• For lesson scheduling, please refer to the lesson calendar.