Mask shortage during epidemics and pandemics: a scoping review of interventions to overcome limited supply
Bibliographic record
Abstract
OBJECTIVE: To characterise published evidence regarding preclinical and clinical interventions to overcome mask shortages during epidemics and pandemics. DESIGN: Systematic scoping review. SETTINGS: All healthcare settings relevant to epidemics and pandemics. SEARCH STRATEGY: English peer-reviewed studies published from January 1995 to June 2020 were included. Literature was identified using four databases (Medline-OVID, EMBASE, CINAHL, Cochrane Library), forwards-and-backwards searching through Scopus and an extensive grey literature search. Assessment of study eligibility, data extraction and evidence appraisal were performed in duplicate by two independent reviewers. RESULTS: Of the 11 220 database citations, a total of 47 articles were included. These studies encompassed six broad categories of conservation strategies: decontamination, reusability of disposable masks and/or extended wear, layering, reusable respirators, non-traditional replacements or modifications and stockpiled masks. Promising strategies for mask conservation in the context of pandemics and epidemics include use of stockpiled masks, extended wear of disposable masks and decontamination. CONCLUSION: There are promising strategies for overcoming face mask shortages during epidemics and pandemics. Further research specific to practical considerations is required before implementation during the COVID-19 pandemic.
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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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".