Housing Vulnerability in Times of Crisis. Impact of the COVID-19 Pandemic on Service Needs and Implementation for Individuals Experiencing Housing Instability in Three Urban Areas of Quebec, Canada
Bibliographic record
Abstract
People experiencing housing instability were particularly vulnerable during the pandemic, with a limited capacity to meet their own needs and to implement public health measures mandated by public authorities. Community organizations had to adapt their social services in response. Based on qualitative research conducted in 2020 and 2021 with individuals experiencing housing instability (n = 35) and community workers (n = 20) in three urban areas of the province of Quebec, this paper analyzes not only the impact of COVID-19 on the daily lives of people experiencing housing instability, but also the ways in which community organizations have taken up the issue and reorganized their interventions. Using the Actor-network theory framework, the findings suggest that the pandemic increased housing vulnerability. COVID-19 further destabilized the primary needs of many service users, including food distribution and access to emergency shelter. While meeting these needs is essential, people also reported difficulties with social isolation and a lack of direct support from community workers. This suggests that housing security goes beyond simply having a home. Community organizations suggested solutions, including developing additional sites to support services outside of central areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".