Efficacy and Use of Cloth Masks: A Scoping Review
Bibliographic record
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, there has been a global shortage of personal protective equipment (PPE). In this setting, cloth masks may play an important role in limiting disease transmission; however, current literature on the use of cloth masks remains inconclusive. This review aims to integrate current studies and guidelines to determine the efficacy and use of cloth masks in healthcare settings and/or the community. Evidence-based suggestions on the most effective use of cloth masks during a pandemic are presented. Embase, MEDLINE, and Google Scholar were searched on March 31, 2020, and updated on April 6, 2020. Studies reporting on the efficacy, usability, and accessibility of cloth masks were included. Additionally, a search of guidelines and recommendations on cloth mask usage was conducted through published material by international and national public health agencies. Nine articles were included in this review after full-text screening. The clinical efficacy of a face mask is determined by the filtration efficacy of the material, fit of the mask, and compliance to wearing the mask. Household fabrics such as cotton T-shirts and towels have some filtration efficacy and therefore potential for droplet retention and protection against virus-containing particles. However, the percentage of penetration in cloth masks is higher than surgical masks or N95 respirators. Cloth masks have limited inward protection in healthcare settings where viral exposure is high but may be beneficial for outward protection in low-risk settings and use by the general public where no other alternatives to medical masks are available.
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.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".