Creating Effective, Evidence-Based Video Communication of Public Health Science (COVCOM Study): Protocol for a Sequential Mixed Methods Effect Study
Bibliographic record
Abstract
BACKGROUND: The nonlinear nature of contagious diseases and the potential for exponential growth can be difficult to grasp for the general public. This has strong implications for public health communication, which needs to be both easily accessible and efficient. A pandemic is an extreme situation, and the accompanying strict societal measures are generally easier to accept if one understands the underlying reasoning behind them. Bringing about informed attitude change and achieving compliance to strict restrictions requires explanations of scientific concepts and terminologies that laypersons can understand. OBJECTIVE: The aim of the project is to develop effective, evidence-based modes of video communication for translating complex, but important, health messages about pandemics to both the general population and decision makers. The study uses COVID-19 as a case to learn and prepare society for handling the ongoing and future pandemics, as well as to provide evidence-based tools for the science communication toolbox. METHODS: The project applies a mixed methods design, combining qualitative methods (eg, interviews, observational studies, literature reviews) and quantitative methods (eg, randomized controlled trials [RCTs]). The project brings together researchers from a wide range of academic fields, as well as communication industry professionals. RESULTS: This study has received funding from the Trond Mohn Foundation through the Research Council of Norway's "COVID-19 Emergency Call for Proposals" March 2020. Recruitment and data collection for the exploratory first phase of the project ran from February 2021 to March 2021. Creative communication work started in May 2021, and the production of videos for use in the RCTs in the final phase of the project started in September 2021. CONCLUSIONS: The COVCOM project will take on several grand challenges within the field of communicating science and provide evidence-based tools to the science communication toolbox. A long-term goal of the project is to contribute to the creation of a more resilient health care system by developing communication responses tailormade for different audiences, preparing society for any future pandemic. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34275.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Scholarly communication Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.148 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".