10.F. Workshop: Applying behavioral science for public health communication:insights from global COVID-19 initiatives
Bibliographic record
Abstract
Abstract In the context of a highly contagious virus and a global pandemic, the key to slowing the spread of COVID-19 and successfully transitioning through the phases of the pandemic and into ‘life after COVID', is public adherence to the unprecedented and rapidly evolving behaviour-based government policies. From social distancing and mask wearing to greater vaccine uptake, the successful implementation of public health interventions around the world revolves around effective community engagement and health communication initiatives that are essential for encouraging informed decision making, enabling positive behaviour change, and maintaining trust among the public. The current global crisis has placed behavioural science at the forefront of multidisciplinary pandemic responses and crisis management initiatives on both local and global level. This workshop will build upon the findings from the global research initiative (The International Assessment of COVID-19-related Attitudes, Concerns, Responses and Impacts in Relation to Public Health Policies (iCARE) study), and the authors and panellists will provide insights on how government communication initiatives can be leveraged to incorporate concepts from behavioural sciences in order to improve adherence to preventive behaviours. The structure of the workshop will include two presentations, followed by a panel discussion. In particular, the authors will highlight successful examples and stress the enormous potential of behavioural sciences to improve adherence to public health and government policies at reduced costs. They will aim to increase audience's understanding on theories of behaviour change and complex systems that influence human behaviour, including individual factors, close environment, social, and systems influences. Moreover, they will discuss feasible solutions for strategic use of behaviour change communication in government interventions, including the application of tailored approaches and targeted messaging across a variety of settings to promote the adoption of healthy behaviours and reduce risk taking at the population level. Lastly, the panellists will reflect upon some feasibility issues in a time-sensitive emergency scenario, such as the need for continuous assessment of the drivers of population behaviours and monitoring of communication impacts. Speakers/Panelists Elena Altieri Lead - Behavioural Insights at WHO, Geneva, Switzerland Tanja Kuchenmuller Unit Head Evidence to Policy and Impact, WHO Evidence-informed Policy Network, Geneva, Switzerland Simon L. Bacon Concordia University, MBMC, CIUSSS NIM, Montreal, Canada Kim L. Lavoie University of Quebec at Montreal, MBMC, CIUSSS-NIM, Montreal, Canada Key messages In the context of a global pandemic, application of behavioural science principles into public health communication activities is critical for optimal publics’ adherence to preventive behaviours. Understanding the motivators of engaging in COVID-19 mitigation, within the context of well-defined behavioural theories has had a direct positive impact on several government approaches.
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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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".