Communicating about emerging infectious disease: The importance of research
Bibliographic record
Abstract
Emerging infectious diseases have taken on renewed significance in the public health sector since the 1990s. Worldwide, governments are preparing emergency plans to guide them; their plans acknowledge that communication will be vital in the event of an outbreak. However, much of the emerging infectious disease communication literature deals with one-way transmission of facts to the public by experts. Little attention is paid to how differently various groups conceptualize risk, or to the idea that there is more to communication than the intentional transfer of information. Emerging infectious disease communication is often based in traditional health promotion or emergency/crisis communication literature, where it is assumed that the only ‘enemy’ is the disease, the right course of action is obvious and the expertise (coming from a public health assumed to be value-free) will not be questioned. Research tends to be limited to exploring barriers to understanding or education, to facilitate better message development. Emerging infectious disease communication research should be broadened to include exploration of implicit assumptions about the nature of the problem at hand (and how to deal with it) as well as the concepts of uncertainty, trust, power, values and biases. Recent risk communication theory, whose focus has historically been on more obviously controversial technological/environmental situations, should guide such research, as it would highlight important contextual factors in which to embed emerging infectious disease communication. This article reviews existing emerging infectious disease communications literature, discusses risk communications theories that could broaden emerging infectious disease communication research, and suggests next steps in a research agenda.
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 | 0.136 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.026 | 0.057 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".