Exploring the effects of COVID-19 outbreak control policies on services offered to people experiencing homelessness
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic and subsequent implementation of public health policies exacerbated multiple intersecting systemic inequities, including homelessness. Housing is a key social determinant of health that played a significant part in the front-line defence against COVID-19, posing challenges for service providers working with people experiencing homelessness (PEH). Public health practitioners and not-for-profit organizations (NFPs) had to adapt existing COVID-19 policies and implement novel measures to prevent the spread of disease within congregate settings, including shelters. It is essential to share the perspectives of service providers working with PEH and their experiences implementing policies to prepare for future public health emergencies and prevent service disruptions. METHODS: In this qualitative case study, we explored how service providers in the non-profit sector interpreted, conceptualized, and implemented COVID-19 public health outbreak control policies in Nova Scotia. We interviewed 11 service providers between September and December 2020. Using thematic analysis, we identified patterns and generated themes. Local, provincial, and national policy documents were useful to situate our findings within the first year of the COVID-19 pandemic and contextualize participants' experiences. RESULTS: Implementing policies in the context of homelessness was difficult for service providers, leading to creative temporary solutions, including pop-up shelters, a dedicated housing isolation phone line, comfort stations, and harm reduction initiatives, among others. There were distinct rural challenges to navigating the pandemic, which stemmed from technology limitations, lack of public transportation, and service closures. This case study illustrates the importance of flexible and context-specific policies required to support PEH and mitigate the personal and professional impact on service providers amid a public health emergency. Innovative services and public health collaboration also exemplified the ability to enhance housing services beyond the pandemic. CONCLUSIONS: The results of this project may inform context-specific emergency preparedness and response plans for COVID-19, future public health emergencies, and ongoing housing crises.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".