A systematic review of the international evidence on the effectiveness of COVID-19 mitigation measures in communal rough sleeping accommodation
Bibliographic record
Abstract
BACKGROUND: Accommodations with shared washing facilities increase the risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection for people experiencing rough sleeping and evidence on what interventions are effective in reducing these risks needs to be understood. METHODS: Systematic review, search date 6 December 2022 with methods published a priori. Electronic searches were conducted in MEDLINE, PubMed, Cochrane Library, CINAHL and the World Health Organization (WHO) COVID-19 Database and supplemented with grey literature searches, hand searches of reference lists and publication lists of known experts. Observational, interventional and modelling studies were included; screening, data extraction and risk of bias assessment were done in duplicate and narrative analyses were conducted. RESULTS: Fourteen studies from five countries (USA, England, France, Singapore and Canada) were included. Ten studies were surveillance reports, one was an uncontrolled pilot intervention, and three were modelling studies. Only two studies were longitudinal. All studies described the effectiveness of different individual or packages of mitigation measures. CONCLUSIONS: Despite a weak evidence base, the research suggests that combined mitigation measures can help to reduce SARS-CoV-2 transmission but are unlikely to prevent outbreaks entirely. Evidence suggests that community prevalence may modify the effectiveness of mitigation measures. More longitudinal research is needed. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021292803.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".