Medical knowledge about COVID-19 is travelling at the speed of mistrust: why this is relevant to primary care
Bibliographic record
Abstract
In 2005, the International Health Regulations (IHR) were established by the World Health Organization (WHO) and endorsed by 196 countries. This legally binding framework aimed to improve pandemic preparedness to detect, assess, and respond to public health events through international coordination and collaboration.1 Regrettably, even the most advanced countries have had a difficult time grappling with COVID-19 due to nonadherence to IHR2,3 and world leaders undermining the science of managing COVID-19. This has led to increased deaths due to a lack of testing, contact tracing, vaccine hesitancy, and adherence to public health recommendations. Additionally, the ability to adhere to IHR on pandemic management has been impacted by circulating misinformation (the unintentional dissemination of false information) and disinformation (intentional dissemination of false information with nefarious intent) through social and traditional media platforms.4 Over a 4-month period in 2020, conventional media outlets circulated over...
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How this classification was reachedexpand
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".