Breaking Siloed Policies: Applying a Gender-Based Analysis Plus (GBA+) to Homelessness during Pregnancy in Canada
Bibliographic record
Abstract
Amongst women and gender diverse (WGD) populations experiencing homelessness in Canada, one of the most vulnerable and understudied subgroups are those who are pregnant. A key barrier to accessing housing for this population are policies that lead to siloed sector work and complicated and inaccessible services. Frequent relocation and fragmented access to essential prenatal and postnatal support are the result. Experiences of homelessness for WGD people are distinct from that of cisgender men; the former tend to experience ‘hidden homelessness’ and are more likely to rely on relational, precarious, and sometimes dangerous housing options. The homelessness sector, its policies, and services tend to be cis-male-centric because of the greater visibility of homelessness in cis-men and fail to meet pregnant WGD people’s needs. This paper describes the findings from a one-day symposium that was held in Toronto, Canada, in June 2023 that aimed to address the siloed approach to housing provision for pregnant WGD people experiencing homelessness. A key focus was to understand how to incorporate a gendered and intersectional discourse into practice and policy. Adopting a gender-based analysis plus (GBA+) approach within policymaking can help illuminate and address why certain groups of WGD people are disproportionately affected by homelessness, including Indigenous Peoples, recent immigrants, racialized people, and those experiencing intimate partner violence, poverty, and substance use.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.010 |
| 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".