Exploring COVID-19 vaccine uptake, confidence and hesitancy among people experiencing homelessness in Toronto, Canada: protocol for the <i>Ku-gaa-gii pimitizi-win</i> qualitative study
Bibliographic record
Abstract
INTRODUCTION: People experiencing homelessness are at high risk for COVID-19 and poor outcomes if infected. Vaccination offers protection against serious illness, and people experiencing homelessness have been prioritised in the vaccine roll-out in Toronto, Canada. Yet, current COVID-19 vaccination rates among people experiencing homelessness are lower than the general population. This study aims to characterise reasons for COVID-19 vaccine uptake and hesitancy among people experiencing homelessness, to identify strategies to overcome hesitancy and provide public health decision-makers with information to improve vaccine confidence and uptake in this priority population. METHODS AND ANALYSIS: qualitative study (formerly the COVENANT study) will recruit up to 40 participants in Toronto who are identified as experiencing homelessness at the time of recruitment. Semistructured interviews with participants will explore general experiences during the COVID-19 pandemic (eg, loss of housing, social connectedness), perceptions of the COVID-19 vaccine, factors shaping vaccine uptake and strategies for supporting enablers, addressing challenges and building vaccine confidence. ETHICS AND DISSEMINATION: Approval for this study was granted by Unity Health Toronto Research Ethics Board. Findings will be communicated to groups organising vaccination efforts in shelters, community groups and the City of Toronto to construct more targeted interventions that address reasons for vaccine hesitancy among people experiencing homelessness. Key outputs will include a community report, academic publications, presentations at conferences and a Town Hall that will bring together people with lived expertise of homelessness, shelter staff, leading scholars, community experts and public health partners.
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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 | 0.034 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".