9.B. Scientific session: Building a resilient society: global behavioural science to improve health emergency management
Bibliographic record
Abstract
Abstract Societal resilience is not only about healthcare capacity and infrastructure - it also depends on human behaviour, mental well-being, and social cohesion. This roundtable workshop's objective is to demonstrate the role of cross-national social and behavioural data for purpose of shaping health emergency preparedness policy, drawing on evidence from two multinational cohort studies (the European Preparedness and Behaviour Study (4 countries); and the global iCARE Study, which assessed public attitudes and behaviours during COVID-19 (75+ countries)) and a national approach from Finland, whose approach to resilience is shaped by its position on Europe's eastern frontier. In the first presentation, RIVM (Netherlands) and ISCIII (Spain) introduce findings from the first wave of the European Preparedness and Behaviour Survey. These studies analyse citizen perceptions of personal, employer, and government preparedness, influenced by past experiences, knowledge gaps, and systemic barriers. Preparedness profiles-based on mental health, resilience, social support, and institutional trust-are presented with tailored policy recommendations by country and sector. In the second presentation, NIJZ (Slovenia) and Ireland's Department of Health will discuss key aspects to integrating behavioural science into preparedness planning: understanding why behavioural data is so important and policy buy-in. Slovenia's findings reveal gaps between intention and behaviour and offer insight into behavioural readiness for future public health crises, while Ireland's case shows how such behavioural evidence is shaping policy. Next, iCARE presents first results from six countries (Canada, Ireland, France, Italy, Australia, Colombia), identifying behavioural drivers behind low future preparedness and proposing interventions based on behavioural theory (presentation 3). Next, iCARE demonstrates how results can be used in policy, based on a collaboration with Canada's Public Health Agency to apply real-time behavioural data to guide national policy on vaccination, testing, and prevention (presentation 4). Finally, THL (Finland) presents a distinct national approach, focusing on citizen resilience amid overlapping crises. Shifting away from individual or military definitions, this initiative highlights social ties, institutional trust, and inclusive communication as foundations of collective resilience, and how they inform policy. The second half of the workshop features a moderated discussion by ECDC and RIVM experts, debating three policy-relevant themes: • Trade-offs between global and local priorities in joint data collection. • A social science knowledge infrastructure to build a resilient Europe. • Broadening preparedness strategies to an all-hazard approach. This workshop aligns with the EPH theme ‘Investing for sustainable health and well-being’ by showing how investment in understanding human behaviour, mental resilience, and social cohesion, strengthens both immediate preparedness and long-term public health sustainability. Key messages • Resilience depends on effective mobilisation of behaviour, mental well-being, and social cohesion - not just infrastructures. • Multi-national behavioural monitoring behaviour contributed to turning preparedness plans into real-world behaviours, revealing universal challenges and local priorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.133 | 0.059 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".