COVID-19 prevalence and infection control measures at homeless shelters and hostels in high-income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has disproportionately impacted homeless populations and service workers, especially within homeless shelter/hostel settings. To date, there have been few evidence syntheses examining outbreaks of COVID-19 among both homeless shelter residents and service workers and no critical review of infection control and prevention (IPAC) measures. This scoping review offers a much-needed synthesis of COVID-19 prevalence within homeless shelters and a review of pertinent IPAC measures. METHODS: We conducted a scoping review that aimed to synthesize academic and gray literature published from March 2020 to July 2021 pertaining to (1) the prevalence of COVID-19 among both residents and staff in homeless shelters and hostels in high-income countries and (2) COVID-19 IPAC strategies applied in these settings. Two reviewers independently screened the literature from several databases that included MEDLINE, PsycInfo, and the WHO's COVID-19 Global Health Portal. The extracted data was mapped, categorized, and thematically discussed. RESULTS: Thematic analysis of 77 academic and gray literature documents revealed four key themes: (1) the demographics of COVID-19 in homeless shelters, (2) asymptomatic spread, (3) pre-existing vulnerability of people experiencing homelessness and shelters, and (4) the inconsistency and ineffectiveness of IPAC implementation. CONCLUSION: This review offers a useful glimpse into the landscape of COVID-19 outbreaks in homeless shelters/hostels and the major contributing factors to these events. This review revealed that there is no clear indication of generally accepted IPAC standards for shelter residents and workers. This review also illustrated a great need for future research to establish IPAC best practices specifically for homeless shelter/hostel contexts. Finally, the findings from this review reaffirm that homelessness prevention is key to limiting disease outbreaks and the associated negative health outcomes in shelter populations. Limitations of this review included the temporal and database constraints of the search strategy, the exclusion of quality assessments of the literature, and the absence of investigation on the influence of emerging variants on public health policy. SYSTEMATIC REVIEW REGISTRATION: This scoping review has not been registered on any database; the protocol is available on York University's Institutional Repository https://dx.doi.org/10.25071/10315/38513 .
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 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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".