Community Stakeholders’ Perceptions of the Impact of the Coronavirus Pandemic on Homelessness in Canada
Bibliographic record
Abstract
Homelessness was already a well-known risk factor contributing to premature death, morbidity, mental illness, and substance use disorder. The coronavirus disease for 2019 (COVID-19) pandemic has amplified disparities in Canada’s public health system, disproportionately impacting people experiencing homelessness. The present study aimed to investigate the impact of the COVID-19 pandemic on the homelessness situation in Canada from community stakeholders’ perceptions. The study used qualitative research approaches underlain by focused ethnography tenets. The sample includes 200 service providers from 28 communities across Canada (at least one site in each province and territory) to participate in virtual focus groups. Data analysis followed a four-step ethnographic approach for thematic analysis in qualitative research. Six main themes emerged: (a) system changes precipitated by the COVID-19 pandemic; (b) personal changes in life circumstances; (c) previous strategies no longer working; (e) opportunities; (d) some things getting better; (f) an overall increase in first time and recurrent homelessness in Canada. The study findings underscored mechanisms required to help ‘tip the scale’ in affording people experiencing homelessness the opportunity to avoid or exit 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".