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Education Academy

4P-CAN Education Academy aims to empower and educate young stakeholders, healthcare professionals, media representatives, and others, fostering a deeper understanding of cancer prevention. Through knowledge sharing and comprehensive education, we strive to equip individuals with the tools and insights needed to make informed decisions and drive positive change in the fight against cancer.

The Education Academy will help establish a robust educational program that guarantees the sustainability of cancer prevention efforts in the medium and long term, even after the conclusion of the project.

By equipping individuals with the knowledge and skills to comprehend the significance of social innovation in cancer prevention, the Education Academy aims to cultivate a pool of dedicated human resources capable of championing and advancing these initiatives well into the future.

Mission

  • Adhere to open science principles and promote research transparency.
  • Build trust in science, improve research quality and efficiency, and foster acceptance of innovation at the societal level.
  • Provide free access to the results of 4P-CAN activities.
  • Offer training and lectures on the specialized topics addressed in 4P-CAN.
  • The Education Academy leverages social media platforms, individual partners, research findings, and policy recommendations to make a societal impact in cancer prevention and primary care.

Register for the Education Academy

📝 Format: Six online modules
📅 When: March–April 2026
Registration form

Social Determinants of Health: Networks, Data, and Methods

Module I:

Sociological instruments for measuring social determinants of health

Instructor(s): Iulian Oană

Date: 06.03.2026; 16:00-19:00 CET (online)

Course description

This course introduces participants to the study of social determinants of health and to the quantitative methods used to measure and analyze them. It explores how socio-cultural factors shape health outcomes and how these can be systematically assessed through data-driven research. Participants will learn how to design and interpret social research using surveys, variables, and statistical analysis, gaining a practical understanding of how quantitative methods support evidence-based health and policy decisions.

By the end of this session, participants will be able to:

  1. Explain what social determinants of health are and why their measurement is essential for public health and policy.
  2. Distinguish between inductive and deductive research approaches and understand their implications for study design.
  3. Conduct key steps in the quantitative research process, from conceptualization and hypothesis formulation to method selection.
  4. Identify and classify different types of variables used in social and health research.
  5. Design, evaluate, and test survey instruments following best practices.
  6. Interpret descriptive and inferential statistical analyses, including hypothesis testing and regression.
  7. Reflect on the value and limitations of quantitative data in understanding complex social and health phenomena.
Module II

Social and Personal Network Analysis – Introductory notions

Instructor(s): Iulian Oană

Date: 13.03.2026; 16:00-19:00 CET (online)

Course description

This course provides an introduction to Social Network Analysis (SNA) and Personal Network Analysis (PNA), focusing on their theoretical foundations, methodological principles, and applications in health and social research. Participants will explore how networks can be used to represent, describe, and analyze relationships between social actors, and how structural and compositional characteristics influence behavior, health, and well-being. The session also covers the main types of network data, their representation, and key measures used to describe network properties. Finally, participants will learn how network processes such as homophily, influence, and context affect social outcomes, and will reflect on ethical considerations in collecting and analyzing relational data.

By the end of this session, participants will be able to:

  1. Define social and personal networks and distinguish Social Network Analysis from other research approaches.
  2. Describe the conceptual and methodological specificities of Personal Network Analysis.
  3. Identify nodes, relations, and types of network data, including how to manage missing or incomplete information.
  4. Distinguish between major types of networks (directed/undirected, unimodal/bimodal, binary/weighted) and their data formats (matrices and edgelists).
  5. Describe networks in terms of composition and structure using node-level and network-level indicators.
  6. Explain the key network processes relevant to health research, such as assortativity, homophily, social influence, and contextual constraints.
  7. Recognize and reflect on ethical issues related to the collection, analysis, and publication of network data.
Module III

Methods and Techniques in SNA / PNA research

Instructor(s): Iulian Oană

Date: 20.03.2026; 16:00-19:00 CET (online)

Course description

This course introduces participants to methods of collecting and managing social network data in both bounded (sociocentric) and unbounded (egocentric) contexts. It emphasizes practical understanding of how to design and implement network questionnaires. Participants will explore the logic and structure of sociocentric and personal network surveys, including name generators, alter-alter ties, and types of data collected at ego and alter levels. The session also covers issues of sampling in network research, the implications of data interdependence for analysis. It also provides an overview of Network Canvas software as a tool for building digital survey instruments for network studies.

By the end of this session, participants will be able to:

  1. Explain the principles of sociocentric and egocentric approaches to gathering network data.
  2. Design basic sociocentric and personal network questionnaires tailored to specific research contexts.
  3. Differentiate between ego-level, alter-level, and relational (alter–alter) data, and understand their analytical value.
  4. Apply appropriate sampling techniques for network studies, including methods for studying hidden or hard-to-reach populations.
  5. Recognize the implications of data interdependence and discuss the limitations of using standard statistical tests on relational data.
  6. Have an overview of Network Canvas as a tool for creating structured protocols for personal network data collection.
Module IV

Applications of PNA in the 4P-CAN Living lab: BMI / Obesity normalization; Smoking behaviours; Eating behaviours; COVID-19 vaccination

Instructor(s): Iulian Oană

Date: 02.04.2026; 16:00-19:00 CET (online)

Course description

This course introduces participants to how network analysis can be applied to understand health-related behaviors such as body weight perception, smoking, eating habits, and vaccination attitudes. It contrasts conventional individual-level approaches with network-based perspectives that emphasize the role of social influence, shared norms, and interpersonal contexts in shaping health behaviors. The session uses the 4P-CAN studies as case examples, guiding participants through the design, data collection, and analytical strategies used to examine personal networks and their effects on health perceptions and decisions.

By the end of this session, participants will be able to:

  1. Identify the difference between standard survey approaches and network-oriented studies in the study of BMI perception, smoking, eating behaviors, and COVID-19 vaccination.
  2. Explain how network analysis provides additional insights compared to standard individual-level approaches.
  3. Describe the objectives, sampling strategies, and data collection design of the 4P-CAN studies.
  4. Identify and interpret ego-level, alter-level, and network-level characteristics within the 4P-CAN datasets.
  5. Understand the analytical methods used to study associations between personal network structure and health behaviors.
  6. Reflect on the empirical findings and their implications for public health research and interventions.
Module V

Visualization of network data and public engagement

Instructor(s): Iulian Oană; Bianca-Elena Mihăilă

Date: 09.04.2026; 16:00-19:00 CET (online)

Course description

This course explores how social and personal network visualizations can be used as powerful tools for public engagement, science communication, and collaborative decision-making. Participants will learn conceptual frameworks for designing and interpreting network visualizations that communicate complex relational data to non-specialist audiences. The session emphasizes interpretability, storytelling, and ethics in visualization practices.

By the end of the session, participants will be able to:

  1. Explain the role of visualization in making network data accessible to the public.
  2. Identify design principles that enhance clarity and engagement.
  3. Critically evaluate examples of network visualizations used in media, policymaking, and participatory research.
  4. Recognize ethical and interpretive challenges in representing people and relationships visually.
  5. Conceptualize how network visualizations can foster dialogue and participation beyond academia.
Module VI

Network analysis and health policies

Instructor(s): Bianca-Elena Mihăilă; Antonin Tron-Lozai

Date: 17.04.2026; 16:00-19:00 CET (online)

Course description

This course introduces how network analysis can be applied to understand, design, and evaluate public health policies. Participants will explore how stakeholder relationships and flows of authority, information, and financing, and stakeholder influence shape policy outcomes, and how participatory tools (like Net-Map) and collaborative frameworks (like Living Labs) help translate network insights into real-world interventions.

By the end of this session, participants will be able to:

  1. Explain the role of network analysis in public policy processes.
  2. Identify and map key stakeholders, resources, and different relations in policy networks.
  3. Understand and critically assess the Net-Map method as a participatory tool for policy design and evaluation.
  4. Understand how Living Labs operationalize networks for policy innovation and co-creation.
  5. Reflect on the ethical and practical implications of using network analysis in policy contexts.

Instructors

Antonin Tron-Lozai

TRON-LOZAI, ANTONIN, is a researcher and toolkit developer at the Centre for Innovation in Medicine (InoMed). Antonin's main academic interests lay in quantitative data analysis and the epistemology and ethics of scientific research. Currently, he is involved as a researcher in the 4P-CAN project, which investigates cancer and cardiovascular risk factors through personal network analysis. He also is a toolkit developer for stakeholder mapping practices, expanding the Net-Map methodology for techniques of online interviews and automated data capture.
Antonin Tron-Lozai

Antonin Tron-Lozai

TRON-LOZAI, ANTONIN, is a researcher and toolkit developer at the Centre for Innovation in Medicine (InoMed). Antonin's main academic interests lay in quantitative data analysis and the epistemology and ethics of scientific research. Currently, he is involved as a researcher in the 4P-CAN project, which investigates cancer and cardiovascular risk factors through personal network analysis. He also is a toolkit developer for stakeholder mapping practices, expanding the Net-Map methodology for techniques of online interviews and automated data capture.

Bianca-Elena Mihăilă

MIHĂILĂ, ELENA-BIANCA, PhD, is a network data scientist, mixed-methods sociologist, and research expert at the Centre for Innovation in Medicine (InoMed). As part of the 4P-CAN project, she employs quantitative methodology, social statistics, personal network analysis, and participatory network-based methods (Net-Map) with the goal to contribute to the understanding of how different health outcomes circulate within personal networks, thereby enhancing the development of evidence-based, well-tailored public policies.
Bianca-Elena Mihăilă

Bianca-Elena Mihăilă

MIHĂILĂ, ELENA-BIANCA, PhD, is a network data scientist, mixed-methods sociologist, and research expert at the Centre for Innovation in Medicine (InoMed). As part of the 4P-CAN project, she employs quantitative methodology, social statistics, personal network analysis, and participatory network-based methods (Net-Map) with the goal to contribute to the understanding of how different health outcomes circulate within personal networks, thereby enhancing the development of evidence-based, well-tailored public policies.

Iulian Oană

OANĂ, IULIAN, PhD, is a network data scientist, quantitative sociologist, and research expert at the Centre for Innovation in Medicine (InoMed). Currently, he is working as a researcher in the 4P-CAN project, where, together with the project team, applies personal network analysis for the understating of health opinions and behaviors of persons inside a living lab, to better public health policies regarding cancer and cardiovascular risk factors.
Iulian Oană

Iulian Oană

OANĂ, IULIAN, PhD, is a network data scientist, quantitative sociologist, and research expert at the Centre for Innovation in Medicine (InoMed). Currently, he is working as a researcher in the 4P-CAN project, where, together with the project team, applies personal network analysis for the understating of health opinions and behaviors of persons inside a living lab, to better public health policies regarding cancer and cardiovascular risk factors.

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