Concerns around public health recommendations on face mask use among individuals who are not medically diagnosed with COVID-19 supported by a systematic review search for evidence.
Bibliographic record
Abstract
Abstract BackgroundContradicting and inconsistent public health recommendations regarding face mask use have been provided to individuals who are not yet medically diagnosed with COVID-19, which is significantly a large population. Face masks are being used by individuals who are not medically diagnosed with COVID-19 as a means to limit the spread of COVID-19 in several countries around the world. While some countries recommend the use of face masks, other countries strictly do not recommend their use to limit the transmission of COVID-19 among individuals who are not medically diagnosed with COVID-19. This paper critically analyses public health recommendations provided to this population regarding face mask use by public health and health professionals of different countries supported by a systematic review that searched for evidence on face mask use among this specific population in limiting the spread of COVID-19.MethodsTo carry out the systematic review portion of this paper, databases Cochrane Library, EMBASE, Google Scholar, PubMed, and Scopus were searched for relevant studies. Two groups of keywords were combined: those relating to face masks and COVID-19.ResultsThe systematic review search did not find any studies that investigated the effectiveness of face mask use in limiting the spread of COVID-19 among those who are not medically diagnosed with COVID-19 to support current public health recommendations.ConclusionsThe finding of the systematic review search, which is a lack of scientific evidence, questions the basis of inconsistent public health recommendations that have been provided to the public at a very early yet a crucial stage of an outbreak. A closer attention need to be given to the procedures and practices behind providing public health guidelines and recommendations during an outbreak by public health and health professionals around the world. This paper calls for 1) evidence-based public health recommendations; 2) considerations when providing public health recommendations in the absence of evidence; 3) evidence and knowledge transparency on current public health recommendations; 4) global alignment on public health recommendations; and 5) further research to strengthen public health recommendations.
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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 | 0.081 | 0.354 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".