Responding to COVID-19 with integrative health and sheltering models for persons experiencing homelessness in Canada: A case study exploring implementation
Bibliographic record
Abstract
Introduction: Throughout the COVID-19 pandemic, persons experiencing homelessness in Canada were disproportionately put at high risk for infection and adverse health outcomes, which prompted many regions to respond by adopting integrative health and sheltering programs to address the unique health inequities faced by this population. Objectives: We aimed to deepen our understanding of the experiences and processes related to the adoption of such integrative health and sheltering programs by exploring the implementation and sustainability of two regional responses in Southern Ontario. Methods: Our study used a multiple case-study design to investigate how different regions responded and implemented integrative health and sheltering models during the pandemic within two mid-sized cities in Southern Ontario. Members of our research team participated in iterative consultations with relevant stakeholders from local agencies and organizations to ground the study’s objectives, data collection strategies, and interview guides in community-based feedback. Using a purposive sampling strategy, we recruited program staff from varying levels of leadership (i.e., front-line health and social care providers, program managers, regional executives) across both regions (n=16) to participate in semi-structured interviews. Data analysis occurred in four stages: 1) narrative descriptions of each site were created; 2) interview transcripts were coded using NVivo 14 and analyzed by members of the research team through line-by-line emergent coding to identify main themes; 3) main themes were cross-referenced and aligned with domains from the Consolidated Framework for Implementation Research (CFIR) to enrich understanding; and 4) analysis culminated in cross-case comparison between sites. Results and impact: The CFIR-informed coding generated multiple themes which aligned with the domains of the framework (innovation, outer setting, inner setting, individuals, and implementation process). Each thematic domain was organized based on implementation barriers and facilitators. Notable barriers included: poor integration between service sectors; limited funding and staffing resources; and differing organizational philosophies. Whereas key facilitators included: trusting relationships amongst partners and staff; prioritizing immediate solution-oriented approaches, especially given the context of the pandemic; and breaking down silos through a shared sense of responsibility. In addition to the CFIR-informed thematic domains, emergent coding further captured themes which spoke to the unique contextual impact of the pandemic on the implementation process. Lessons learned: Although integrative health and sheltering programs were implemented to reduce disease transmission among persons experiencing homelessness during a global health crisis, uniting both health and social care in a collaborative model consequently fostered a shared sense of responsibility across diverse stakeholders. This highlighted the importance of simultaneously targeting multiple social determinants of health to improve overall health outcomes for this population. Integrative health and sheltering models have the potential to consolidate and thereby strengthen funding, personnel support, and service delivery for persons experiencing homelessness beyond the pandemic, which can work towards dismantling health equities faced by this population and advancing efforts to addressing this longstanding public health issue in other countries amid the fallout of COVID-19. Next steps: Using our findings, we will collaborate with project partners to co-design implementation guides for other regions interested in developing similar integrative programs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".