Structural violence as a driver of COVID-19 vaccine hesitancy and low vaccine uptake among people experiencing homelessness in Toronto, Canada: A qualitative study
Bibliographic record
Abstract
BACKGROUND: People experiencing homelessness are at increased risk of contracting SARS-CoV-2 and of severe complications of COVID-19. Vaccination is promoted as a key strategy to protect against severe illness from SARS-CoV-2 infection, but rates of vaccination among people experiencing homelessness are lower than the general population. Studies suggest lower uptake is a result of vaccine hesitancy, but few theoretically engage with the structural drivers of vaccine hesitancy. We explore the role of structural violence in shaping COVID-19 vaccine decision-making among people experiencing homelessness. METHODS: We conducted this critical qualitative study in Toronto, Canada. Thirty-one adults of diverse races, genders, and vaccination status participated in in-depth interviews between November 2021 and February 2022. Ecosocial Theory shaped the study focus, interview guide, and analysis. Analysis employed an abductive thematic approach guided by the Framework Method analytic approach. FINDINGS: Participant experiences were shaped by multiple forms of structural violence. Analytic themes included: i) challenges navigating income generation and 'placelessness' during lockdowns; ii) perceived and enacted stigma and discrimination, and feeling 'othered' as a result of vaccine mandates; and iii) a disruption in the continuity of access to healthcare; all were housed under the domains of economic and social deprivation, social trauma, and inadequate medical care. These shaped participant's description of their agency and subsequent vaccine decision-making, concerns related to COVID-19 vaccination, and ultimately (re)produced health inequities. INTERPRETATION: Multiple pathways of structural violence experienced prior to and during the COVID-19 pandemic influenced vaccine decision-making and represent critical mechanisms by which health inequity becomes embodied by people experiencing homelessness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".