Planning Shelter Service Spaces and Structures for Resilience: A Spatial Analysis of Women’s Shelters During COVID-19
Bibliographic record
Abstract
Objective: Globally, domestic violence against women increased in severity over the course of the COVID-19 pandemic. Shelters supporting women experiencing temporary homelessness due to violence had to make major changes in service delivery to accommodate pandemic protocols, including how many families could stay in shelter, where they were allowed to reside, and how they were, and were not, able to interact with shelter staff and the outside world. The present study used a novel approach to spatial analysis to understand how pandemic protocols affected shelter space use. Method: Floor plans and written pandemic protocols were submitted by 15 women’s shelters in Ontario, Canada. Each pandemic protocol was analyzed and mapped onto its respective floor plans to determine how much space was lost under different modes of operation: normal (pre-pandemic), physical distancing (using pandemic protocols), and quarantine (using pandemic protocols during an outbreak). Three types of shelter space were analyzed to understand what types of spaces shelters were losing: Primary (bedrooms, bathrooms, and laundry areas), Secondary (community areas and staff offices), and Tertiary (hallways and storage). Findings: All 15 shelters lost space, with an average of 27% of net area lost overall (range 7%-56%). Within the three types of space, 18% of Primary, 48% of Secondary, and <1% of Tertiary space was lost. Key factors influencing space loss were the type of protocol used and the existing layout of the shelter space. Conclusions: Recommendations for shelter space planning in the context of rapidly evolving public health requirements are provided.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".