Responding to COVID-19 with integrative health and sheltering models for persons experiencing homelessness in Southern Ontario, Canada: protocol for a qualitative study exploring implementation and sustainability
Bibliographic record
Abstract
INTRODUCTION: COVID-19 has disproportionately impacted persons experiencing homelessness in Canada, who are at an increased risk of infection and severe outcomes. In response to the pandemic, several regions have adopted programmes that aim to address the intersecting nature of health and social challenges faced by persons facing homelessness. These programmes adopted during the pandemic may contribute to broader health and social impacts beyond limiting COVID-19 transmission, but the processes involved in developing and implementing these types of programmes and their sustainability after the pandemic are unknown. Our overall goal is to understand the processes of developing and implementing integrative health and sheltering initiatives in Ontario during COVID-19, as well as their sustainability post-pandemic. METHODS AND ANALYSIS: This study will use a multiple case study design-two cases over 1 year-enabling us to investigate how integrative health and sheltering approaches have been implemented in two mid-sized cities in Ontario, Canada. Each case will offer a unique narrative; through cross-case analysis, the cases will highlight programme operations, successes and challenges. Data will be collected using semi-structured interviews with programme staff and managers, and document analysis. Project partners will be brought together to further explore and interpret findings, along with co-creating a sustainability action plan and policy documents. ETHICS AND DISSEMINATION: Ethics clearance was obtained through the Western University Research Ethics Board and the University of Waterloo Office of Research Ethics. Findings will be disseminated through publications, conference presentations and lay summary reports.
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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 | 0.041 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".