Women’s experiences of homelessness and violence during the COVID-19 pandemic in Canada’s largest city: an integrated qualitative analysis of clients of violence against women organizations and encampment residents
Bibliographic record
Abstract
Abstract Background Violence against women (VAW) is the most common cause of women’s homelessness. However, policy and programming for VAW and homelessness have developed and operated in siloes in many countries, including Canada, limiting capacity to address the unique needs of women facing both interrelated issues. This study uniquely analyzes data from participants experiencing violence and homelessness drawn from each of the VAW and homelessness sectors at the height of the COVID-19 pandemic in Canada’s largest city, Toronto. Methods We qualitatively analyzed data from two studies conducted concurrently in 2021 as part of the Marginalization and COVID-19 (MARCO) Project, which aimed to investigate outcomes of the COVID-19 response among people experiencing marginalization in the Greater Toronto Area. Participants were 10 survivors who accessed VAW services and 23 residents of homeless encampments. We applied a reflexive thematic analysis within a feminist poststructuralist framework to explore participants’ experiences of violence and homelessness and interrogate the structural factors that dictate which and how different participants ‘end up’ in different sectors and their outcomes. Results We generated three themes in our analysis: (1) inequities exacerbated: (abuse of) power and control as pathways into women’s homelessness; (2) negotiating trade-offs between safety and autonomy; and (3) gender stereotypes versus gender-transformative approaches. There was a common pathway of VAW to homelessness, exacerbated by the COVID-19 pandemic and most acutely felt by participants facing intersecting forms of marginalization (e.g., economic or immigration precarity). Considerations around safety and autonomy were central to shaping experiences of women’s homelessness. Participants described ways in which gender stereotypes, both internalized and at systems- and organizational-levels, harmed them in terms of service (in)access – especially for women who used substances or were lone caregivers. The most positive experiences when accessing shelter or housing services were when participants received gender-specific supports that promoted their safety and empowerment – in many cases, in spite of the constraints presented by COVID-19 public health measures. Conclusions Our results highlight the need for strengthened collaboration between the VAW and housing/homelessness sectors and a unified policy strategy to address homelessness that applies a gender-transformative and intersectional approach, during and beyond public health emergencies.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".