Communication strategies and media discourses in the age of COVID-19: an urgent need for action
Bibliographic record
Abstract
Identified in December 2019 in China, the coronavirus 2019 (COVID-19) has been declared a Public Health Emergency of International Concern (PHEIC). Pandemics share features that increase fear. While some fear can stimulate preventive health behaviors, extreme fear can lead to adverse psychological and behavioral response. The media play a major role shaping these responses. When dealing with a PHEIC, the authorities' communication strategies are embedded in a multilevel governance and a highly hierarchal system, which adds another layer of complexity. Carrying out more 'real-world research' is crucial to generate evidence relating to the psychosocial and behavioral aspects involved during the COVID-19 pandemic and how it is shaped by authorities and media discourses. Interdisciplinary research and international collaborations could contribute to improve our understanding and management of risk information. Emerging from a socio-ecological perspective, future research must integrate multilevel analytical elements, to ensure triangulation of evidence and co-constructing robust recommendations. A mixed-method approach should be privileged to address these issues. At the micro-level, a population-based survey could be conducted in various jurisdictions to assess and compare psychosocial issues according to sociocultural groups. Then, a quantitative/qualitative discourse analysis of the media could be performed. Finally, a network analysis could allow assessing how official information flows and circulates across levels of governance. The COVID-19 represents an opportunity to evaluate the impacts of information/communication strategy and misinformation on various cultural and socioeconomic groups, providing important lessons that could be applied to future health emergencies and disasters.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".