Effectiveness of using full personal protective equipment in reducing the transmission of SARS-CoV-2 in health care workers: A systematic review
Bibliographic record
Abstract
BACKGROUND: This systematic review aimed to assess the effectiveness of full personal protective equipment (PPE) in preventing COVID-19 transmission among health care workers. METHODS: Studies published from December 2019 to August 2024 were searched in MEDLINE, Embase, Cochrane Library, CINAHL, Epistemonikos, ClinicalTrials.gov, MedRxiv, and Web of Science. Full PPE was defined as the combination of respiratory protection (surgical mask, N95, or equivalents), eye protection (visor or goggles), gown, and gloves, as recommended by the World Health Organization (WHO). The comparator was partial PPE or no PPE. Two reviewers independently performed screening, data extraction, and risk of bias assessment. The review was registered on PROSPERO (CRD4202230259). RESULTS: Eight observational studies were included; 4 showed a significant reduction in transmission with full PPE. Seven studies compared full to partial PPE, and 1 compared full PPE to no protection. Among studies with significant results, odds ratios ranged from 0.03 to 0.6. Risk of bias was critical in 6 studies and serious in 2. No meta-analysis was performed due to study quality. CONCLUSION: Full PPE appears protective for health care workers. The small number and low quality of studies limit the certainty of this conclusion. Further analyses are required to establish clear guideline for its use.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".