Respiratory infectious disease outbreaks among people experiencing homelessness: a systematic review of prevention and mitigation strategies
Bibliographic record
Abstract
PURPOSE: People experiencing homelessness (PEH) are at increased risk of respiratory infections and associated morbidity and mortality. To characterize optimal intervention strategies, we completed a systematic review of mitigation strategies for PEH to minimize the spread and impact of respiratory infectious disease outbreaks, including COVID-19. METHODS: The study protocol was registered in PROSPERO (#2020 CRD42020208964) and was consistent with the preferred reporting in systematic reviews and meta-analyses guidelines. A search algorithm containing keywords that were synonymous to the terms "Homeless" and "Respiratory Illness" was applied to the six databases. The search concluded on September 22, 2020. Quality assessment was performed at the study level. Steps were conducted by two independent team members. RESULTS: A total of 4468 unique titles were retrieved with 21 meeting criteria for inclusion. Interventions included testing, tracking, screening, infection prevention and control, isolation support, and education. Historically, there has been limited study of intervention strategies specifically for PEH across the world. CONCLUSIONS: Staff and organizations providing services for people experiencing homelessness face specific challenges in adhering to public health guidelines such as physical distancing, isolation, and routine hygiene practices. There is a discrepancy between the burden of infectious diseases among PEH and specific research characterizing optimal intervention strategies to mitigate transmission in the context of shelters. Improving health for people experiencing homelessness necessitates investment in programs scaling existing interventions and research to study new approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".