COVID-19 Community Response Team for Toronto Homeless Services and Congregate Living Settings: an evaluation of Hospital-Community partnership through COVID-19 vaccine provision
Bibliographic record
Abstract
Individuals experiencing homelessness face unique physical and mental health challenges, increased morbidity and premature mortality. In Canada, it is estimated that 235,000 individuals experience homelessness annually, and 180,000 use emergency shelters each night. (1) COVID -19 creates a significant heightened risk for those living in congregate sheltering spaces. Individuals with a recent history of homelessness and diagnosed with COVID-19, are at significantly higher risk of hospitalization and death than those housed in Ontario communities (2). Women’s College Hospital (WCH) is an ambulatory hospital situated in downtown Toronto. In March 2020, WCH set up one of Toronto’s 14 COVID-19 assessment centres to facilitate free testing for SARS-CoV-2. Formed by a group of health care providers at WCH, the goal of the COVID-19 Community Response Team (CRT) was to support Toronto shelters and congregate living sites to manage and prevent outbreaks of SARS-CoV-2 using a collaborative model through onsite mobile testing; supporting the management and prevention of outbreaks; and providing infection prevention and control training and guidance. (3) In total, CRT leveraged this model of care with 49 shelter and congregate living sites from April 2020 to April 2021. From this, the WCH COVID-19 vaccine program emerged, where 14 shelters were regionally identified to co-design and support the administration of vaccine clinics within each sheltering site. This research seeks to evaluate the impact and importance of this partnership model and its future potential in community-centered integrated care. In this study, three areas of inquiry are addressed: (1) Vaccine program evaluation and lessons learned (e.g., What were barriers, facilitators, and lessons throughout the process? How were shelter staff and clients impacted?); (2) Perceptions on hospital/community partnership (e.g., What were overall perceptions of this partnership and strategy?); (3) Opportunities forward (e.g., How can this partnership between hospitals and shelters be sustained in the future to fulfill needs beyond COVID-19). Constructivist grounded theory (CGT) is used in this project to explore perceptions and experiences of this partnership. (4) CGT data analysis revealed five main categories, 16 subcategories, and one core category. The core category is “access to healthcare is a human right; understand our communities”. The main categories are COVID-19 response capacity, outbreak identification and management, barriers to the vaccine program, community-centred immediate shelter needs, and avenues for intersectoral relationship strengthening. In conclusion, three key takeaways emerged for health(care) policy and practice: 1.‘Health as a human right’ framework is an organizing principle in shelters but not necessarily in hospitals. How can hospitals adopt and integrate this framework at the policy level to operationalize an equity-based approach to care? 2.For hospitals, there are gaps in knowledge about community and shelter realities. Ongoing formal partnering between hospitals and communities is one way to bridge this gap. 3.Empowering shelter staff is crucial to the success of hospital-partnered programs and clinical interventions. Finally, this project calls attention to the urgent context-specific exploration needed to advance official hospital-community partnerships, where there is an everlasting commitment and accountability.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".