Lessons Learned: COVID 19 and Individuals Experiencing Homelessness in the Global Context
Bibliographic record
Abstract
The coronavirus disease (COVID-19) pandemic has affected all our lives but did not affect all parts of societies equally. This study uses a systematic literature review approach to examine the experiences of homeless populations during COVID-19. Our literature review identified lessons learned and promising practices from the field at a global level, and summarizes academic studies in order to promote future efforts to prepare homeless populations for potential extreme events in the future. Forty-one of 209 articles were selected to prepare this literature review. Following the academic database search, grey literature from various organizations were also identified to enrich the literature results and analysis. Findings from these articles were grouped under three main themes to better illustrate the results: (1) impact of COVID-19 on people experiencing homelessness (PEH), (2) support mechanisms, and (3) promising practices. A comparative approach also was used to examine how PEH responded during two previous pandemics (severe acute respiratory syndrome [SARS] in 2003 and Swine Flu 2009) compared to the COVID-19 pandemic. Findings showed that there was continuous improvement in the disaster preparedness for PEH during COVID-19 when compared to past pandemics. In addition, promising practices have emerged. However, ongoing issues, such as lack of personal protective equipment (PPE), staff shortages, and communication problems, still persist in the field. More research regarding PEH during pandemics is needed, and their voices should be included.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".