Evolution of WHO COVID-19 mask guidelines amid intense demands for rapid advice
Bibliographic record
Abstract
During a health emergency, there is an urgent need to rapidly develop guidelines that meet minimum quality standards, as exemplified by the development of WHO guidelines on mask use in health care and community settings during the COVID-19 pandemic. Between January 2020 and October 2023, WHO developed 21 guideline updates on the use of masks as part of infection prevention and control (IPC) practices. Guideline developers had to deal with an ever-growing volume of evidence of variable quality. Initially, indirect evidence drawn from other severe respiratory illnesses and established minimum requirements for IPC were used. As direct evidence began to emerge, WHO commissioned a living systematic review on mask use in June 2020, which formed the basis of evidence-to-decision making. As more evidence became available, additional considerations were incorporated into the process of recommendation formulation, including harms, acceptability, feasibility and resource use. Target populations for the mask guidelines expanded to include the general public, including children. A broad range of disciplines supported guideline development, including IPC, epidemiology, infectious diseases, occupational health, engineering, pneumology, paediatrics, and water, sanitation and hygiene, as well as civil society representatives. Additional expertise was engaged in the areas of ventilation and aerobiology to expand the range of perspectives regarding modes of transmission. Despite challenges, the experience of rapidly and regularly updating advice on mask use during an emergency health response has shown that it is possible to apply the minimum standards for ensuring the guideline methodology is trustworthy and transparent, with increasing rigor over time as evidence improves. Overall, the experience of developing guidelines during the COVID-19 pandemic underscores the importance of adapting to evolving evidence, incorporating diverse perspectives, and maintaining transparency to ensure a rigorous methodology and in return guideline trustworthiness and effectiveness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".