“They forgot about us”: experiences of the COVID-19 pandemic among people deprived of housing in an urban centre in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: People deprived of housing have been disproportionately affected by the COVID-19 pandemic and the public health mitigation measures implemented in response. Emerging evidence has shown the adverse health outcomes experienced by these communities due to SARS-CoV-2 infection; however, the voices of community members themselves have not been widely amplified in the published literature. METHODS: We conducted an interpretive qualitative study. People deprived of housing were involved in study development, recruitment, and data analysis. People deprived of housing or precariously housed were recruited during street outreach from June to July 2020. Participants completed one-on-one semi-structured interviews that were audio-recorded, transcribed, and analyzed thematically. RESULTS: Twenty-one participants were interviewed. Central to participants' experiences of the COVID-19 pandemic were descriptions of access to services, in terms of both changes in service availability and the reality of how accessible existing services were to the community, represented by the theme access. Four other themes were generated from our analysis and include feeling and being unheard, stripped of dignity, I've been broken, and strength and survival (with a subtheme, community care). CONCLUSION: Future emergency response efforts must meaningfully engage people deprived of housing in planning and decision-making in order to minimize adverse impacts of health emergencies and the associated public health responses. There needs to be more careful consideration of the unintended harmful impacts of public health measures implemented in response to pandemics.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".