Increasing pandemic vaccination rates with effective communication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Communicating effectively with the public about the importance of vaccination during a pandemic poses a challenge to health communicators. The public's concerns about the safety, effectiveness and necessity of vaccines lead many people to refuse vaccination and the current communication strategies are often unsuccessful at overcoming the public's resistance to vaccinate. Convincing the public to receive a vaccination, especially during a pandemic when there can be so much uncertainty about the vaccine and the disease, requires a revised communication approach. This revised approach should integrate into messages information that the public identifies as important, as well as presenting messages in a way that is consistent with our evolved social learning biases. These biases will impact both the content of the message and who delivers the message to different target populations. Additionally, an improved understanding between media and health communicators about the role each plays during a crisis may increase the effectiveness of messages disseminated to the public. Lastly, given that the public is increasingly seeking health information from on-line and other electronic sources, health communication needs to continue to find ways to integrate new technologies into communication strategies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it